This web mapping application shows the monitoring networks used to track drought conditions across Manitoba. Each tab displays a different source of data, including: streamflow and water level, groundwater, precipitation, reservoir supply status, and Canadian and United States Drought Monitor contours. Each of the data sources are explained in more detail below. Please note the following information when using the web mapping application: When you click on a data point on the River and Lake, Groundwater or Reservoir maps, a pop-up box will appear. This pop-up box contains information on the streamflow (in cubic feet per second; ft3/s), water level (in feet), groundwater level (in metres), storage volume (acre-feet), or supply status (in per cent of full supply level; %) for that location. Click on the Percentile Plot link at the bottom of the pop-up box to view a three-year time series of observed conditions (available for river and lake and groundwater conditions only). A toolbar is located in the top right corner of the web mapping application. The Query Tool can be used to search for a specific river, lake or reservoir monitoring station by name or aquifer type by location. The Layer List enables the user to toggle between precipitation conditions layers (1-month, 3-month, and 12-month) and increase or decrease the transparency of the layer. Data is current for the date indicated on the pop-up box, percentile plot, or map product. Near-real time data are preliminary and subject to change upon review. River and lake conditions are monitored to determine the severity of hydrological dryness in a watershed. River and lake measurements are converted to percentiles by comparing daily measurements from a specified day to historical measurements over the monitoring station’s period of record for that particular day. A percentile is a value on a scale of zero to 100 that indicates the percent of a distribution that is equal to or below it. In general: Streamflow (or lake level) which is greater than the 90th percentile is classified as “much above normal”. Streamflow (or lake level) which is between the 75th and 90th percentile is classified as “above normal”. Streamflow (or lake level) which is between the 25th and 75th percentiles is classified as “normal”. Streamflow (or lake level) which is between the 10th and 25th percentile is classified as “below normal”. Streamflow (or lake level) which is less than the 10th percentile is classified as “much below normal”. "Median" indicates the midpoint (or 50th percentile) of the distribution, whereby 50 per cent of the data falls below the given point, and 50 per cent falls above. Other flow categories include: "Lowest" indicates that the estimated streamflow (or lake level) is the lowest value ever measured for the day of the year. "Highest" indicates that the estimated streamflow (or lake level) is the highest value ever measured for the day of the year. Monitoring stations classified as “No Data” do not have current estimates of streamflow (or lake level) available. Click on the Percentile Plot link at the bottom of the pop-up box to view a graph (in PDF format) displaying a three-year time series of observed conditions relative to the historical percentiles described above. The period of record used to compute the percentiles is stated, alongside the station ID, and if the river or lake is regulated (i.e. controlled) or natural. Hydrometric data are obtained from Water Survey of Canada, Manitoba Infrastructure, and the United States Geological Survey. Near real-time data are preliminary as they can be impacted by ice, wind, or equipment malfunction. Preliminary data are subject to change upon review. Groundwater conditions are monitored to determine the severity of hydrological dryness in an aquifer. Water levels are converted to percentiles by comparing daily measurements from a specified day to historical measurements over the monitoring station’s period of record for that particular day. A percentile is a value on a scale of zero to 100 that indicates the percent of a distribution that is equal to or below it. In general: A groundwater level which is greater than the 90th percentile is classified as “much above normal”. A groundwater level which is between the 75th and 90th percentile is classified as “above normal”. A groundwater level which is between the 25th and 75th percentiles is classified as “normal”. A groundwater level which is between the 10th and 25th percentile is classified as “below normal”. A groundwater level which is less than the 10th percentile is classified as “much below normal”. Monitoring stations classified as “No Data” do not have current measurements of groundwater level available. "Median" indicates the midpoint (or 50th percentile) of the distribution, whereby 50 per cent of the data falls below the given point, and 50 per cent falls above. Click on the Percentile Plot link at the bottom of the pop-up box to view a graph (in PDF format) displaying a three-year time series of observed conditions relative to the historical percentiles described above. The period of record used to compute the percentiles is stated, alongside the station ID. Precipitation conditions maps are developed to determine the severity of meteorological dryness and are also an indirect measurement of agricultural dryness. Precipitation indicators are calculated at over 40 locations by comparing total precipitation over the time period to long-term (1971 – 2015) medians. Three different time periods are used to represent: (1) short-term conditions (the past month), (2) medium-term conditions (the past three months), and (3) long-term conditions (the past twelve months). These indicator values are then interpolated across the province to produce the maps provided here. Long-term and medium-term precipitation indicators provide the most appropriate assessment of dryness as the short term indicator is influenced by significant rainfall events and spatial variability in rainfall, particularly during summer storms. Due to large distances between meteorological stations in northern Manitoba, the interpolated contours in this region are based on limited observations and should be interpreted with caution. Precipitation conditions are classified as follows: Per cent of median greater than 115 per cent is classified as “above normal”. Per cent of median between 85 per cent and 115 per cent is classified as “normal”. Per cent of median between 60 per cent and 85 per cent is classified as “moderately dry”. Per cent of median between 40 per cent and 60 per cent is classified as a “severely dry”. Per cent of median less than 40 per cent is classified as an “extremely dry”. Precipitation data is obtained from Environment and Climate Change Canada, Manitoba Agriculture, and Manitoba Sustainable Development’s Fire Program. Reservoir conditions are monitored at 15 locations across southern Manitoba to track water availability, including possible water shortages. Conditions are reported both as a water level and as a “supply status”. The supply status is the current amount of water stored in the reservoir compared to the target storage volume of the reservoir (termed “full supply level”). A supply status greater than 100 per cent represents a reservoir that is exceeding full supply level. Canadian and U.S Drought Monitors: Several governments, agencies, and universities monitor the spatial extent and intensity of drought conditions across Canada and the United States, producing maps and data products available through the Canadian Drought Monitor and United States Drought Monitor websites. The Canadian Drought Monitor is managed through Agriculture and Agri-Food Canada, while the United States Drought Monitor is a joint effort between The National Drought Mitigation Centre (at the University of Nebraska-Lincoln), the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. The drought monitor assessments are based on a suite of drought indicators, impacts data and local reports as interpreted by federal, provincial/state and academic scientists. Both the Canadian and United States drought assessments have been amalgamated to form this map, and use the following drought classification system: D0 (Abnormally Dry) – represents an event that occurs every 3 - 5 years; D1 (Moderate Drought) – 5 to 10 year event; D2 (Severe Drought) – 10 to 20 year event; D3 (Extreme Drought) – 20 to 50 year event; and D4 (Exceptional Drought) – 50+ year event. Additionally, the map indicates whether drought impacts are: (1) short-term (S); typically less than six months, such as impacts to agriculture and grasslands, (2) long-term (L); typically more than six months, such as impacts to hydrology and ecology, or (3) a combination of both short-term and long-term impacts (SL). The Canadian Drought Monitor publishes its assessments monthly, and United States Drought Monitor maps are released weekly on Thursday mornings. The amalgamated map provided here will be updated on a monthly basis corresponding to the release of the Canadian Drought Monitor map. Care will be taken to ensure both maps highlight drought conditions for the same point in time; however the assessment dates may differ between Canada and the United States due to when the maps are published. Please click on an area of drought on the map to confirm the assessment date. Canadian Drought Monitor data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. United States Drought Monitor data are available on the United States Drought Monitor website: https://droughtmonitor.unl.edu. For more information, please visit the Manitoba Drought Monitor website.

The Property Valuation Data Listing offered by BatchData delivers an extensive and detailed dataset designed to provide unparalleled insight into real estate market trends, property values, and investment opportunities. This dataset includes over 9 critical data points that offer a comprehensive view of property valuations across various geographic regions and market conditions. Below is an in-depth description of the data points and their implications for users in the real estate industry.

The Property Valuation Data Listing by BatchData is categorized into four primary sections, each offering detailed insights into different aspects of property valuation. Here’s an in-depth look at each category:

1. Current Valuation AVM Value as of Specific Date: The Automated Valuation Model (AVM) estimate of the property’s current market value, calculated as of a specified date. This value reflects the most recent assessment based on available data. Use Case: Provides an up-to-date valuation, essential for making current investment decisions, setting sale prices, or conducting market analysis. Valuation Confidence Score: A measure indicating the confidence level of the AVM value. This score reflects the reliability of the valuation based on data quality, volume, and model accuracy. Use Case: Helps users gauge the reliability of the valuation estimate. Higher confidence scores suggest more reliable values, while lower scores may indicate uncertainty or data limitations.

2. Valuation Range Price Range Minimum: The lowest estimated market value for the property within the given range. This figure represents the lower bound of the valuation spectrum. Use Case: Useful for understanding the potential minimum value of the property, helping in scenarios like setting a reserve price in auctions or evaluating downside risk. Price Range Maximum: The highest estimated market value for the property within the given range. This figure represents the upper bound of the valuation spectrum. Use Case: Provides insight into the potential maximum value, aiding in price setting, investment analysis, and comparative market assessments. AVM Value Standard Deviation: A statistical measure of the variability or dispersion of the AVM value estimates. It indicates how much the estimated values deviate from the average AVM value. Use Case: Assists in understanding the variability of the valuation and assessing the stability of the estimated value. A higher standard deviation suggests more variability and potential uncertainty.

3. LTV (Loan to Value Ratio) Current Loan to Value Ratio: The ratio of the outstanding loan balance to the current market value of the property, expressed as a percentage. This ratio helps assess the risk associated with the loan relative to the property’s value. Use Case: Crucial for lenders and investors to evaluate the financial risk of a property. A higher LTV ratio indicates higher risk, as the property value is lower compared to the loan amount.

4. Valuation Equity Calculated Total Equity: based upon estimate amortized balances for all open liens and AVM value Use Case: Provides insight into the net worth of the property for the owner. Useful for evaluating the financial health of the property, planning for refinancing, or understanding the owner’s potential gain or loss in case of sale.

This structured breakdown of data points offers a comprehensive view of property valuations, allowing users to make well-informed decisions based on current market conditions, valuation accuracy, financial risk, and equity potential.

This information can be particularly useful for: - Automated Valuation Models (AVMs) - Fuel Risk Management Solutions - Property Valuation Tools - ARV, rental data, building condition and more - Listing/offer Price Determination

Wind Spacecraft:

The Wind spacecraft (https://wind.nasa.gov) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo. The instruments used for this data product are the fluxgate magnetometer (MFI) [Lepping et al., 1995] and the radio receivers (WAVES) [Bougeret et al., 1995]. The MFI measures 3-vector B_o at ~11 samples per second (sps); WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density and also takes time series snapshot/waveform captures of electric and magnetic field fluctuations, called TDS bursts herein.

WAVES Instrument:

The WAVES experiment [Bougeret et al., 1995] on the Wind spacecraft is composed of three orthogonal electric field antenna and three orthogonal search coil magnetometers. The electric fields are measured through five different receivers: Low Frequency FFT receiver called FFT (0.3 Hz to 11 kHz), Thermal Noise Receiver called TNR (4-256 kHz), Radio receiver band 1 called RAD1 (20-1040 kHz), Radio receiver band 2 called RAD2 (1.075-13.825 MHz), and the Time Domain Sampler (TDS). The electric field antenna are dipole antennas with two orthogonal antennas in the spin plane and one spin axis stacer antenna.

The TDS receiver allows one to examine the electromagnetic waves observed by Wind as time series waveform captures. There are two modes of operation, TDS Fast (TDSF) and TDS Slow (TDSS). TDSF returns 2048 data points for two channels of the electric field, typically E_x and E_y (i.e. spin plane components), with little to no gain below ~120 Hz (the data herein has been high pass filtered above ~150 Hz for this reason). TDSS returns four channels with three electric(magnetic) field components and one magnetic(electric) component. The search coils show a gain roll off ~3.3 Hz [e.g., see Wilson et al., 2010; Wilson et al., 2012; Wilson et al., 2013 and references therein for more details].

The original calibration of the electric field antenna found that the effective antenna lengths are roughly 41.1 m, 3.79 m, and 2.17 m for the X, Y, and Z antenna, respectively. The +E_x antenna was broken twice during the mission as of June 26, 2020. The first break occurred on August 3, 2000 around ~21:00 UTC and the second on September 24, 2002 around ~23:00 UTC. These breaks reduced the effective antenna length of Ex from ~41 m to 27 m after the first break and ~25 m after the second break [e.g., see Malaspina et al., 2014; Malaspina & Wilson, 2016].

TDS Bursts:

TDS bursts are waveform captures/snapshots of electric and magnetic field data. The data is triggered by the largest amplitude waves which exceed a specific threshold and are then stored in a memory buffer. The bursts are ranked according to a quality filter which mostly depends upon amplitude. Due to the age of the spacecraft and ubiquity of large amplitude electromagnetic and electrostatic waves, the memory buffer often fills up before dumping onto the magnetic tape drive. If the memory buffer is full, then the bottom ranked TDS burst is erased every time a new TDS burst is sampled. That is, the newest TDS burst sampled by the instrument is always stored and if it ranks higher than any other in the list, it will be kept. This results in the bottom ranked burst always being erased. Earlier in the mission, there were also so called honesty bursts, which were taken periodically to test whether the triggers were working properly. It was found that the TDSF triggered properly, but not the TDSS. So the TDSS was set to trigger off of the Ex signals.

A TDS burst from the Wind/WAVES instrument is always 2048 time steps for each channel. The sample rate for TDSF bursts ranges from 1875 samples/second (sps) to 120,000 sps. Every TDS burst is marked a unique set of numbers (unique on any given date) to help distinguish it from others and to ensure any set of channels are appropriately connected to each other. For instance, during one spacecraft downlink interval there may be 95% of the TDS bursts with a complete set of channels (i.e., TDSF has two channels, TDSS has four) while the remaining 5% can be missing channels (just example numbers, not quantitatively accurate). During another downlink interval, those missing channels may be returned if they are not overwritten. During every downlink, the flight operations team at NASA Goddard Space Fligth Center (GSFC) generate level zero binary files from the raw telemetry data. Those files are filled with data received on that date and the file name is labeled with that date. There is no attempt to sort chronologically the data within so any given level zero file can have data from multiple dates within. Thus, it is often necessary to load upwards of five days of level zero files to find as many full channel sets as possible. The remaining unmatched channel sets comprise a much smaller fraction of the total.

All data provided here are from TDSF, so only two channels. Most of the time channel 1 will be associated with the E_x antenna and channel 2 with the E_y antenna. The data are provided in the spinning instrument coordinate basis with associated angles necessary to rotate into a physically meaningful basis (e.g., GSE).

TDS Time Stamps:

Each TDS burst is tagged with a time stamp called a spacecraft event time or SCET. The TDS datation time is sampled after the burst is acquired which requires a delay buffer. The datation time requires two corrections. The first correction arises from tagging the TDS datation with an associated spacecraft major frame in house keeping (HK) data. The second correction removes the delay buffer duration. Both inaccuracies are essentially artifacts of on ground derived values in the archives created by the WINDlib software (K. Goetz, Personal Communication, 2008) found at https://github.com/lynnbwilsoniii/Wind_Decom_Code.

The WAVES instrument's HK mode sends relevant low rate science back to ground once every spacecraft major frame. If multiple TDS bursts occur in the same major frame, it is possible for the WINDlib software to assign them the same SCETs. The reason being that this top-level SCET is only accurate to within +300 ms (in 120,000 sps mode) due to the issues described above (at lower sample rates, the error can be slightly larger). The time stamp uncertainty is a positive definite value because it results from digitization rounding errors. One can correct these issues to within +10 ms if using the proper HK data.

*** The data stored here have not corrected the SCETs! ***

The 300 ms uncertainty, due to the HK corrections mentioned above, results from WINDlib trying to recreate the time stamp after it has been telemetered back to ground. If a burst stays in the TDS buffer for extended periods of time (i.e., >2 days), the interpolation done by WINDlib can make mistakes in the 11^th significant digit. The positive definite nature of this uncertainty is due to rounding errors associated with the onboard DPU (digital processing unit) clock rollover. The DPU clock is a 24 bit integer clock sampling at ∼50,018.8 Hz. The clock rolls over at ∼5366.691244092221 seconds, i.e., (16*2²⁴)/50,018.8. The sample rate is a temperature sensitive issue and thus subject to change over time. From a sample of 384 different points on 14 different days, a statistical estimate of the rollover time is 5366.691124061162 ± 0.000478370049 seconds (calculated by Lynn B. Wilson III, 2008). Note that the WAVES instrument team used UR8 times, which are the number of 86,400 second days from 1982-01-01/00:00:00.000 UTC.

The method to correct the SCETs to within +10 ms, were one to do so, is given as follows:

1. Retrieve the DPU clock times, SCETs, UR8 times, and DPU Major Frame Numbers from the WINDlib libraries on the VAX/ALPHA systems for the TDSS(F) data of interest.
2. Retrieve the same quantities from the HK data.
3. Match the HK event number with the same DPU Major Frame Number as the TDSS(F) burst of interest.
4. Find the difference in DPU clock times between the TDSS(F) burst of interest and the HK event with matching major frame number (Note: The TDSS(F) DPU clock time will always be greater than the HK DPU clock if they are the same DPU Major Frame Number and the DPU clock has not rolled over).
5. Convert the difference to a UR8 time and add this to the HK UR8 time. The new UR8 time is the corrected UR8 time to within +10 ms.
6. Find the difference between the new UR8 time and the UR8 time WINDlib associates with the TDSS(F) burst. Add the difference

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Switched source data from collecting records from each state independently to using the WestDAAT dataset v1.0 - Initial public release Description In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, as well as geospatial data defining the areas in which water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. In addition, while all states make their water rights publicly available, each provides their records in unique formats, meaning that file types, field availability, and terms vary from state to state. This leads to additional challenges to managing resources which crossmore » state lines, or conducting consistent multi-state water analyses. For the first version of HarDWR, we collected the water rights databases from 11 Western States of the United States. In order to preform regional analyses with the collected data, the raw records had to be harmonized into one single format. The Water Data Exchange (WaDE) is a program dedicated to the sharing of water-related data for the Western U.S. in a singular consistent format. Created by the Western States Water Council (WSWC) to facilitate the collection and dissemination of water data among WSWC's member states and the public, WaDE provides an important service for those interested in water resource planning and management in their focus region. Of the services which WaDE provides, the one of the most interesting is the WestDAAT dataset, which is a collection of water rights data provided by the 18 WSWC member states that have been standardized into a single format, much like we had done on a more limited scale with HarDWR v1. For this version of HarDWR we decided to use WestDAAT, specifically a snapshot created in Feburary 2024, as our water rights source data. A full explanation of the benefits gained from this switch can be found in the description of the updated Harmonized Water Rights Records v2.0, but in short it has allowed us to focus more of our efforts on answering research questions and gaining a more realistic understanding of how water rights are allocated. For more information on how the data for WestDAAT was collected, please see the WaDE data summary. Terms of Use While WaDE works directly with the state agencies to collect and standardize the water rights records, the ultimate authority for the water rights data remains the individual states. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. As several of the states update their water rights databases daily, the information provided here is not the latest possible, and should not be used for legal purposes. WestDAAT itself has irregular updates. Additional questions about the data the source states provided should be directed to the respective state agencies (see methods.csv and organization.csv files described below). In addition, although data was presented here was not collected directly from the states, several states requested specifically worked disclaimers when sharing their data. These disclaimers are included here as an acknowledgement from where the water rights data is primarily sourced. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." File Descriptions The unmodified February, 2024 WestDAAT snapshot is composed of nine files. Below is a brief description of each file, as well as how they were utilized for HarDWR. WaDEDataDictionaryTerms.xlsx: As the file's name implies, this is a data dictionary for all of the below named files. This file describes the column names for each of the following files, with the exception of citation.txt which does not have any columns. The descriptions for each file are divided by tab,with the same name as their associated file, within this document. allocationamount.csv: The "main" file of the group, it contains the water right records for each state. Of particular note, each water right is broken down into one or more water allocations. Allocations may be withdrawn from one or more locations, or even multiple allocations associated with a particular location. This is a more subtle and realistic representation of how water is used than what was available in the first version of HarDWR. For the records from some states, this can mean that multiple allocations listed under a single right will appear as rows within this file. citation.txt: A combination of contact information for WaDE personnel, disclaimer about how the data should be used, and guidelines for citing WestDAAT. methods.csv: A file describing the source and method by which WaDE collected water rights data from each state. organization.csv: A file listing the water rights authoritative agencies for each state. sites.csv: This file provides the geographic, and other descriptors, of the physical location of allocations, called 'sites'. To reiterate, it is possible for one allocation to be associated with multiple sites, as well as one site to be associated with multiple allocations. The two descriptors which we were most interested in where the site's coordinates, as well as whether the site was classified as a Point of Diversion (POD) or a Place of Use (POU). As a general rule, PODs are geographic points, while POUs are areas typically represented as property boundaries or irregularly shaped polygons. sites_pouGeometry.csv: For those allocations with a POU site, this file contains the defining points for the associated polygons. variables.csv: A file describing the units in which an allocation's water amount is reported within WestDAAT. This information is essentially a repeat of the 'AllocationFlow_CFS' and 'AllocationVolume_AF' columns within allocationamount.csv, at least for our purposes. watersources: This file describes the source of water from which each site extracts from. For our purposes, this table was used to determine whether the water came from Surface Water, Groundwater, or Unspecified Water.« less

NGC 3293 is a young stellar cluster at the northwestern periphery of the Carina Nebula Complex that has remained poorly explored until now. The authors wanted to characterize the stellar population of NGC 3293 in order to evaluate key parameters of the cluster population like the age and the mass function, and to test claims of an abnormal initial mass function (IMF) and a deficit of <= 2.5M_sun stars. Thus, they performed a deep (71 ksec) X-ray observation of NGC 3293 with Chandra in which they detected 1026 individual X-ray point sources. These X-ray data directly probe the low-mass (M <= 2M_sun) stellar population by means of the strong X-ray emission of young low-mass stars. The authors have identified counterparts for 74% of the X-ray sources in their deep near-infrared images. These data clearly show that NGC 3293 hosts a large population of ~ 1M_sun stars, refuting claims of a lack of M <= 2.5M_sun stars. The analysis of the color-magnitude diagram suggests an age of ~8-10 Myr for the low-mass population of the cluster. There are at least 511 X-ray detected stars with color-magnitude positions that are consistent with young stellar members within 7 arcminutes from the cluster center. The number ratio of X-ray detected stars in the 1-2 solar mass range versus the M >= 5M_sun stars (known from optical spectroscopy) is well consistent with the expectation from a normal field initial mass function. Most of the early B-type stars and ~20% of the later B-type stars are detected as X-ray sources. These data shows that NGC 3293 is one of the most populous stellar clusters in the entire Carina Nebula Complex (only excelled by Tr 14, and very similar to Tr 16 and Tr 15). The cluster has probably harbored several O-type stars, the supernova explosions of which may have had an important impact on the early evolution of the Carina Nebula Complex. The authors used the Chandra X-ray Observatory to perform a deep pointing of the cluster NGC 3293 with the Imaging Array of the Chandra Advanced CCD Imaging Spectrometer (ACIS-I). The 71-ksec observation was performed as an open time project with ObsID 16648 (PI: T. Preibisch) during Chandra Observing Cycle 15 in October 2015 (start date: 2015-10-07 T10:14:23, end date: 2015-10-08 T06:43:28). The imaging array ACIS-I provides a field of view of 17' x 17' on the sky (which corresponds to a scale of 11.3 x 11.3pc at the cluster distance of 2.3 kpc), and has a pixel size of 0.492". The aimpoint of the observation was RA(J2000) = 10^h 35^m 50.07^s, Dec(J2000) = -58^o 14' 00", which is close to the optical center of the cluster (see Fig. 1 in the reference paper). The pointing roll angle (i.e., the orientation of the detector with respect to the celestial North direction) was 140.19^o. In addition to ACIS-I, one CCD detector (CCD 7 = S3) of the spectroscopic array ACIS-S was also operational during this pointing. It covers an 8.3' x 8.3' area on the sky southwest of the cluster center. While the ACIS-I chips are front-illuminated (FI), the S3 chip is back-illuminated (BI), and thus its response extends to energies below that accessible by the FI chips. This causes a substantially higher level of background in the S3 chip. Furthermore, the PSF is seriously degraded at the rather large off-axis angles of the S3 chip. These two effects led to a considerably higher detection limit for point sources in the area covered by the S3 chip compared to the region covered by the ACIS-I array. Nevertheless, the S3 data were included in the data analysis and source detection, and contributed four point sources to the total source list. At the distance of 2.3 kpc, the expected ACIS point source sensitivity limit for a three-count detection on-axis in a 71-ks observation corresponds to a minimum X-ray luminosity of L_x ~ 10^29.7 erg s^-1 in the 0.5-8.0 keV energy band, assuming an extinction of A_V ~ 1 mag (N_H ~ 2 x 10²¹ cm^-2) typical for the stars in the central region of NGC 3293, and a thermal plasma with kT = 1 keV (which is a typical value for young stars). Using the empirical relation between X-ray luminosity and stellar mass and the temporal evolution of X-ray luminosity from the sample of young stars in the Orion Nebula Cluster, which was very well studied in the Chandra Orion Ultradeep Project (Preibisch et al. 2005, ApJS, 160, 401; Preibisch & Feigelson 2005, ApJS, 160, 390), the authors expected to detect ~90% of the ~ 1M_sun stars in the central region of the young cluster NGC 3293. The X-ray properties of the 97 B-type stars in the ACIS-I field towards the cluster (24 of which are detected as X-ray sources) are not included in this HEASARC table, but are listed in Table 3 of the reference paper, which is also reproduced below:

 ESL No.* Star Name X-ray Spectral Type X-ray Luminosity (Lx) log (Lx/Lbol) Src No. erg/s 49 B2.5 V < 4.33e+30 < -5.88 33 HDE 303073 B8 III < 7.15e+30 < -6.31 65 ALS 20075 B5 III-V < 2.12e+30 < -5.88 77 B6-7 V < 1.42e+30 < -5.91 96 ALS 20084 B6-7 III < 9.09e+29 < -5.96 87 47 B5 V 4.62e+30 -5.11 38 B2.5 V < 7.16e+29 < -6.94 68 78 B9 III 4.79e+30 72 B8 IIp < 6.87e+29 69 B5 V < 3.89e+29 < -6.47 22 HDE 303075 B0.5-1.5n < 6.22e+29 < -7.77 109 B5 V < 5.05e+29 < -6.06 93 B6-7 V < 5.16e+29 < -6.17 116 B6-7 V < 4.74e+29 < -5.88 73 B6-7 V < 3.87e+29 < -6.38 10 CPD-57 3500 395 B1 III 7.35e+29 -7.89 121 ALS 20096 B8: III < 4.84e+29 50 B3 Vn < 5.01e+29 < -6.71 2 HD 91943 418 B0.7 Ib 4.11e+30 -8.15 41 V438 Car B2.5 V < 3.94e+29 < -7.21 48 CPD-57 3505 461 B2.5 V 1.39e+30 -6.67 3 CPD-57 3506A 490 B1 III 5.37e+30 -7.63 125 B8 III-V < 8.62e+29 < -5.48 19 V405 Car 523 B1 V 6.77e+29 -7.88 34 CPD-57 3509 535 B2 IIIh 6.71e+29 -7.54 1 HD 91969 542 B0 Iab 2.78e+31 -7.52 106 565 B6-7 V 1.20e+30 -5.54 53 CPD-57 3512 B3 V < 3.61e+29 < -6.70 98 598 B8 III-V 1.31e+30 -5.65 30 CPD-57 3514 601 B2 V 1.99e+30 -6.64 123 604 B8 III 3.79e+30 -4.98 8 HD 91983 626 B1 III 1.36e+30 -7.78 32 CPD-57 3518 B0.5-B1.5 Vn < 1.20e+30 < -7.14 61 B5 V < 3.87e+29 < -6.56 5 CPD-57 3521 679 B1 III 3.45e+30 -7.61 28 CPD-57 3520 B2 V < 4.16e+29 < -7.46 113 B6-7 V < 4.09e+29 < -6.01 11 CPD-57 3526 703 B1: 2.29e+30 6 CPD-57 3526B 710 B1 III 2.29e+30 -7.73 84 B5 V < 3.99e+29 < -6.33 31 CPD-57 3528 B2 V < 1.50e+30 < -6.66 29 CPD-57 3531 B0.5-B1.5 Vn < 5.99e+29 < -7.56 59 B5 III-Vn < 8.23e+29 < -6.61 80 B5 V < 1.31e+30 < -5.98 13 HD 92024 831 B1 III 6.59e+29 -7.82 108 850 B6-7 V 3.65e+30 -5.09 95 884 B6-7 V 1.49e+30 -5.66 67 B3 V < 1.20e+30 < -6.42 97 B6-7 III < 6.34e+29 < -6.01 94 927 B5 V 4.42e+30 -5.35 85 B5 V < 1.47e+30 < -5.80 4 CPD-57 3523 697 B1 III 3.40e+30 -7.57 7 HD 92044 908 B1 III 2.20e+30 -7.94 14 CPD-57 3524A 704 B0.5 IIIn 5.46e+30 -7.27 

README

WARNING

Below is a template to write a README file for this BIDS dataset. If this message is still present, it means that the person exporting the file has decided not to update the template.If you are the researcher editing this README file, please remove this warning section. The README is usually the starting point for researchers using your dataand serves as a guidepost for users of your data. A clear and informativeREADME makes your data much more usable. In general you can include information in the README that is not captured by some otherfiles in the BIDS dataset (dataset_description.json, events.tsv, ...). It can also be useful to also include information that might already bepresent in another file of the dataset but might be important for users to be aware ofbefore preprocessing or analysing the data. If the README gets too long you have the possibility to create a /doc folderand add it to the .bidsignore file to make sure it is ignored by the BIDS validator. More info here: https://neurostars.org/t/where-in-a-bids-dataset-should-i-put-notes-about-individual-mri-acqusitions/17315/3

Details related to access to the data

• [ ] Data user agreement If the dataset requires a data user agreement, link to the relevant information.
• [ ] Contact person Indicate the name and contact details (email and ORCID) of the person responsible for additional information.
• [ ] Practical information to access the data If there is any special information related to access rights orhow to download the data make sure to include it.For example, if the dataset was curated using datalad,make sure to include the relevant section from the datalad handbook:http://handbook.datalad.org/en/latest/basics/101-180-FAQ.html#how-can-i-help-others-get-started-with-a-shared-dataset ## Overview
• [ ] Project name (if relevant)
• [ ] Year(s) that the project ran If no scans.tsv is included, this could at least cover when the data acquisitionstarter and ended. Local time of day is particularly relevant to subject state.
• [ ] Brief overview of the tasks in the experiment A paragraph giving an overview of the experiment. This should include thegoals or purpose and a discussion about how the experiment tries to achievethese goals.
• [ ] Description of the contents of the dataset An easy thing to add is the output of the bids-validator that describes what type ofdata and the number of subject one can expect to find in the dataset.
• [ ] Independent variables A brief discussion of condition variables (sometimes called contrastsor independent variables) that were varied across the experiment.
• [ ] Dependent variables A brief discussion of the response variables (sometimes called thedependent variables) that were measured and or calculated to assessthe effects of varying the condition variables. This might also includequestionnaires administered to assess behavioral aspects of the experiment.
• [ ] Control variables A brief discussion of the control variables --- that is what aspectswere explicitly controlled in this experiment. The control variables mightinclude subject pool, environmental conditions, set up, or other thingsthat were explicitly controlled.
• [ ] Quality assessment of the data Provide a short summary of the quality of the data ideally with descriptive statistics if relevantand with a link to more comprehensive description (like with MRIQC) if possible. ## Methods ### Subjects A brief sentence about the subject pool in this experiment. Remember that Control or Patient status should be defined in the participants.tsvusing a group column.
• [ ] Information about the recruitment procedure- [ ] Subject inclusion criteria (if relevant)- [ ] Subject exclusion criteria (if relevant) ### Apparatus A summary of the equipment and environment setup for theexperiment. For example, was the experiment performed in a shielded roomwith the subject seated in a fixed position. ### Initial setup A summary of what setup was performed when a subject arrived. ### Task organization How the tasks were organized for a session.This is particularly important because BIDS datasets usually have task dataseparated into different files.)
• [ ] Was task order counter-balanced?- [ ] What other activities were interspersed between tasks?
• [ ] In what order were the tasks and other activities performed? ### Task details As much detail as possible about the task and the events that were recorded. ### Additional data acquired A brief indication of data other than theimaging data that was acquired as part of this experiment. In additionto data from other modalities and behavioral data, this might includequestionnaires and surveys, swabs, and clinical information. Indicatethe availability of this data. This is especially relevant if the data are not included in a phenotype folder.https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html#phenotypic-and-assessment-data ### Experimental location This should include any additional information regarding thethe geographical location and facility that cannot be includedin the relevant json files. ### Missing data Mention something if some participants are missing some aspects of the data.This can take the form of a processing log and/or abnormalities about the dataset. Some examples:
• A brain lesion or defect only present in one participant- Some experimental conditions missing on a given run for a participant because of some technical issue.- Any noticeable feature of the data for certain participants- Differences (even slight) in protocol for certain participants. ### Notes Any additional information or pointers to information thatmight be helpful to users of the dataset. Include qualitative informationrelated to how the data acquisition went.

This dataset contains the derived connectomes, discriminability scores, and classification performance for structural connectomes estimated from a subset of the Nathan Kline Institute Rockland Sample dataset, and is associated with an upcoming manuscript entitled: Numerical Instabilities in Analytical Pipelines Compromise the Reliability of Network Neuroscience. The associated code for this project is publicly available at: https://github.com/gkpapers/2020ImpactOfInstability. For any questions, please contact Gregory Kiar (gkiar07@gmail.com) or Tristan Glatard (tristan.glatard@concordia.ca).

Below is a table of contents describing the contents of this dataset, which is followed by an excerpt from the manuscript pertaining to the contained data.

• impactofinstability_connect_dset25x2x2x20_inputs.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with input perturbations.
• impactofinstability_connect_dset25x2x2x20_pipeline.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with pipeline perturbations.
• impactofinstability_discrim_dset25x2x2x20_both.csv : Discriminability scores for each grouping of the 25x2x2x20 dataset.
• impactofinstability_connect+feature_dset100x1x1x20_both.h5 : Connectomes and features derived from 100 subjects, 1 sessions, 1 subsamples, and 20 MCA simulations with both perturbation types.
• impactofinstability_classif_dset100x1x1x20_both.h5 : Classification performance results for the BMI classification task on the 100x1x1x20 dataset.

Dataset
The Nathan Kline Institute Rockland Sample (NKI-RS) dataset [1] contains high-fidelity imaging and phenotypic data from over 1,000 individuals spread across the lifespan. A subset of this dataset was chosen for each experiment to both match sample sizes presented in the original analyses and to minimize the computational burden of performing MCA. The selected subset comprises 100 individuals ranging in age from 6 – 79 with a mean of 36.8 (original: 6 – 81, mean 37.8), 60% female (original: 60%), with 52% having a BMI over 25 (original: 54%).

Each selected individual had at least a single session of both structural T1-weighted (MPRAGE) and diffusion-weighted (DWI) MR imaging data. DWI data was acquired with 137 diffusion directions; more information regarding the acquisition of this dataset can be found in the NKI-RS data release [1].

In addition to the 100 sessions mentioned above, 25 individuals had a second session to be used in a test-retest analysis. Two additional copies of the data for these individuals were generated, including only the odd or even diffusion directions (64 + 9 B0 volumes = 73 in either case). This allows an extra level of stability evaluation to be performed between the levels of MCA and session-level variation.

In total, the dataset is composed of 100 diffusion-downsampled sessions of data originating from 50 acquisitions and 25 individuals for in depth stability analysis, and an additional 100 sessions of full-resolution data from 100 individuals for subsequent analyses.

Processing
The dataset was preprocessed using a standard FSL [2] workflow consisting of eddy-current correction and alignment. The MNI152 atlas was aligned to each session of data, and the resulting transformation was applied to the DKT parcellation [3]. Downsampling the diffusion data took place after preprocessing was performed on full-resolution sessions, ensuring that an additional confound was not introduced in this process when comparing between downsampled sessions. The preprocessing described here was performed once without MCA, and thus is not being evaluated.

Structural connectomes were generated from preprocessed data using two canonical pipelines from Dipy [4]: deterministic and probabilistic. In the deterministic pipeline, a constant solid angle model was used to estimate tensors at each voxel and streamlines were then generated using the EuDX algorithm [5]. In the probabilistic pipeline, a constrained spherical deconvolution model was fit at each voxel and streamlines were generated by iteratively sampling the resulting fiber orientation distributions. In both cases tracking occurred with 8 seeds per 3D voxel and edges were added to the graph based on the location of terminal nodes with weight determined by fiber count.

Perturbations
All connectomes were generated with one reference execution where no perturbation was introduced in the processing. For all other executions, all floating point operations were instrumented with Monte Carlo Arithmetic (MCA) [6] through Verificarlo [7]. MCA simulates the distribution of errors implicit to all instrumented floating point operations (flop).

MCA can be introduced in two places for each flop: before or after evaluation. Performing MCA on the inputs of an operation limits its precision, while performing MCA on the output of an operation highlights round-off errors that may be introduced. The former is referred to as Precision Bounding (PB) and the latter is called Random Rounding (RR).

Using MCA, the execution of a pipeline may be performed many times to produce a distribution of results. Studying the distribution of these results can then lead to insights on the stability of the instrumented tools or functions. To this end, a complete software stack was instrumented with MCA and is made available on GitHub through https://github.com/gkiar/fuzzy.

Both the RR and PB variants of MCA were used independently for all experiments. As was presented in [8], both the degree of instrumentation (i.e. number of affected libraries) and the perturbation mode have an effect on the distribution of observed results. For this work, the RR-MCA was applied across the bulk of the relevant libraries and is referred to as Pipeline Perturbation. In this case the bulk of numerical operations were affected by MCA.

Conversely, the case in which PB-MCA was applied across the operations in a small subset of libraries is here referred to as Input Perturbation. In this case, the inputs to operations within the instrumented libraries (namely, Python and Cython) were perturbed, resulting in less frequent, data-centric perturbations. Alongside the stated theoretical differences, Input Perturbation is considerably less computationally expensive than Pipeline Perturbation.

All perturbations were targeted the least-significant-bit for all data (t=24and t=53in float32 and float64, respectively [7]). Simulations were performed between 10 and 20 times for each pipeline execution, depending on the experiment. A detailed motivation for the number of simulations can be found in [9].

Evaluation
The magnitude and importance of instabilities in pipelines can be considered at a number of analytical levels, namely: the induced variability of derivatives directly, the resulting downstream impact on summary statistics or features, or the ultimate change in analyses or findings. We explore the nature and severity of instabilities through each of these lenses. Unless otherwise stated, all p-values were computed using Wilcoxon signed-rank tests.

Direct Evaluation of the Graphs
The differences between simulated graphs was measured directly through both a direct variance quantification and a comparison to other sources of variance such as individual- and session-level differences.

Quantification of Variability – Graphs, in the form of adjacency matrices, were compared to one another using three metrics: normalized percent deviation, Pearson correlation, and edgewise significant digits. The normalized percent deviation measure, defined in [8], scales the norm of the difference between a simulated graph and the reference execution (that without intentional perturbation) with respect to the norm of the reference graph. The purpose of this comparison is to provide insight on the scale of differences in observed graphs relative to the original signal intensity. A Pearson correlation coefficient was computed in complement to normalized percent deviation to identify the consistency of structure and not just intensity between observed graphs. Finally, the estimated number of significant digits for each edge in the graph was computed. The upper bound on significant digits is 15.7 for 64-bit floating point data.

The percent deviation, correlation, and number of significant digits were each calculated within a single session of data, thereby removing any subject- and session-effects and providing a direct measure of the tool-introduced variability across perturbations. A distribution was formed by aggregating these individual results.

Class-based Variability Evaluation – To gain a concrete understanding of the significance of observed variations we explore the separability of our results with respect to understood sources of variability, such as subject-, session-, and pipeline-level effects. This can be probed through Discriminability [10], a technique similar to ICC which relies on the mean of a ranked distribution of distances between observations belonging to a defined set of classes.

Discriminability can then be interpreted as the probability that an observation belonging to a given class will be more similar to other observations within that class than observations of a different class. It is a measure of reproducibility, and is discussed in detail in [10].

This definition allows for the exploration of deviations across arbitrarily defined classes which in practice can be any of those listed above. We combine this statistic with permutation testing to test hypotheses on whether

The collection of in situ data is generally a costly process, with the Arctic being no exception. Indeed, there has been a perception that the Arctic lacks for in situ sampling; however, after many years of concerted effort and international collaboration, the Arctic is now rather well sampled with many cruise expeditions every year. For example, the GLODAP product has a greater density of in situ sample points within the Arctic than along the equator. While this is useful for open ocean processes, the fjords of the Arctic, which serve as crucially important intersections of terrestrial, coastal, and marine processes, are sampled in a much more ad hoc process. This is not to say they are not well sampled, but rather that the data are more difficult to source and combine for further analysis. It was therefore noted that the fjords of the Arctic are lacking in FAIR (Findable, Accessible, Interoperable, and Reusable) data. To address this issue a single dataset has been created from publicly available, predominantly in situ data from a number of online platforms. After finding and accessing the data, they were amalgamated into a single project-wide standard, ensuring their interoperability. The dataset was then uploaded to PANGAEA so that it itself can be findable and reusable into the future. The focus of the data collection was driven by the key drivers of change in Arctic fjords identified in a companion review paper. After receiving feedback on this process and the dataset itself, a second version (v2.0) has been created. This dataset is an amalgamation of roughly 1,400 other FAIR datasets. Every datum contained within this dataset is allowed to be redistributed, and every datum is referenced with the appropriate citation. The purpose of combining this many datasets was to provide a single data source within which the interested researcher could investigate a range of socio-ecological processes within Arctic fjords. The focus on the collection of the data was from in situ sources, rather than remotely sensed or model data. Though to provide necessary context across sites for seawater temperature and sea-ice cover, analysed (not raw) data from three remotely sensed products are included.The explanations for the columns are: date_accessed: When the data were accessed URL: The web address where one may download the data citation: The correct citation for the use of the data type: Whether the data are collected 'in situ' or from one of three remotely sensed sources site: generally one of seven primary study sites in the European Arctic, though there are some exceptions. Contractions stand for: kong = Kongsfjorden, is = Isfjorden, stor = Storfjorden, por = Porsangerfjorden, disko = Disko Bay, nuup = Nuup Kangerlua, young = Young Sound, sval = Svalbard, green = Greenland. Site can be useful to check when no lon/lat coordinates are provided for the data. category: The broad category within which the data fall (i.e. cryosphere, physics, chemistry, biology, social) driver: 1 of 14 drivers. Similar to the categories, but more specific (e.g. carbonate system, fisheries, sea-ice, etc.) longitude [°E]: The longitude of the data point in degree decimals. latitude [°N]: The latitude of the data point in degree decimals. date/time [UTC+0]: The date of the data point. Note that all data are either, daily, monthly, or annual, but must ascribe to a YYYY-MM-DD format.* depth [m]: The depth of the data in meters (below the surface of the water). NA values are at the surface, and negative values are on land (e.g. terrestrial runoff measurements)

The massive limestone promontory of Berry Head is probably the most impressive outcrop of Devonian limestone in south Devon. Forming the southern tip of Torbay, the head shelters the fishing port of Brixham, which has grown up in the protection it gives from the prevailing southwesterly gales. Berry Head has a long history; apart from it's importance to shipping, it is surrounded on three sides by the sea and forms and excellent defensive position. There was once an iron age fort here and today the scene is dominated by two Napoleonic forts: one on Berry Head, the other on Oxley Head a few hundred metres to the southwest. To the naturalist the head is better known for the many unusual plants, insects and birds that can be seen there. Among the plants are such spectacular rarities as white rock rose, tree mallow and six species of orchids: the birds include the largest breeding colony of auks (mainly guillemots) on the Channel coast together with a variety of other breeding seabirds. The north side of the headland is scarred by the disused Berry Head Quarry. Work here ceased in 1969 after the quarry had been worked to the limits of the lease and down to sea level. Faced with imminent closure, the quarry company produced a series of proposals to remove even more of the headland, accompanied by increasingly silly ideas for turning the resultant devastation into a tourist attraction. These ranged from landscaping the cliffs with piles of boulders to restaurants, marinas, bridges over the quarry, an ornamental seawater lagoon, and escalator stairways up the cliff. Fortunately they were all rejected. Strangely, until recently Berry Head has received little attention from cavers. Before 1983 the only recorded caves were Ash Hole, a few in the quarry, and the sea caves in the cliffs to the south. Sporadic work by various people turned up other caves , but since they were never recorded they were duly lost again. All this changed in 1983, when Peter Glanvill started a systematic investigation of sea level caves on the south side of the head. In 1984 the Devon Spelaeological Society explored Hogberry Cave at the east end of the quarry. The following year Corbridge Cave was explored, again by DSS. A systematic exploration of the headland was then begun by some DSS members and new finds followed rapidly, with the result that today there are over 50 caves on record. What has been revealed is a complex of coastal caves that is unique in Great Britain. The area can now provide some very interesting caving. The Corbridge-Cavern system offers a maze like system over 200 metres long with a series of tidal pools at low level. Several smaller caves in the quarry are also worth visiting, and some of the quarry caves are well decorated. A collectors piece is the 43 metres deep vertical rift system of Sweetwater Pot, which has one of the deepest sumps in Devon and is reached by an unnerving abseil down the quarry face. Under the quarry floor and on the south side of Berry Head, flooded sea level caves give decidedly different caving, in which careful timing of trips to coincide with low tide can be essential. Further south, the underground lakes of Durl Head Cave and Oxley Head Cave can be explored by boat or swimming, and provide some entertaining diving. A feature of all the sea level caves is the abundant and often spectacular marine life, which even penetrates into the pools of Corbridge Cave, where prawns, eels and other animals have been found. The Berry Head caves have a number of features of particular scientific interest. Most of the caves are solutional in origin, showing the features of phreatic (sub water-table) development. Much of the horizontal passage development is at an altitude of a round 24m O.D, just below the level of an old marine erosion platform visible on the south side of the Head at 28m O.D. Horizontal cave development at low level also lies just below fossil marine platforms, at levels of +8.5m O.D and 4m O.D. The marine platforms mark former periods of higher sea level. Horizontal passage networks probably formed just below sea level, within or at the base of a thin freshwater layer overlying a seawater aquifer which extended under the whole headland (seawater is saturated with calcium carbonate and cannot dissolve limestone to form caves). A second mechanism of cave formation is mechanical erosion by the sea, which has formed the large sea caves to the south of Berry Head. Where the sea has broken into pre-existing solution caves, a hybrid type is seen, where a large sea cave passage leads into a network of smaller solutional passages i.e. Garfish Cave. It is possible that in future Berry Head will prove to be important in the study of former sea levels ; the well preserved caves and their sediments may provide a more detailed record of change than surface evidence which has often been destroyed. The biology of the caves is still being investigated. A small bat colony regularly roosts in the dry caves, but of potentially greater interest is the marine fauna of the flooded low level caves. The most spectacular life is seen in short submerged caves along the coast, and in the threshold zone of larger caves in the same situation. Here the passage walls and roof are covered with encrusting animals. Sponges, hydroids, red sea squirts and anenomes are usually present, with one or two species often completely dominant in any one place. Further in, otherwise bare walls may be dotted with odd sponges, anenomes, cup corals and tube worms competing for existence in an environment where lessened water movement brings less food. Such forms may penetrate well into the dark zone, as in Garfish Cave. The most extreme marine cave environment as yet studied on Berry Head is the underground creek that runs up through Corbridge Cave, connecting a large pool in the west bay of the quarry to the sea. The only water movement is a tidal flow that reverses every few hours as water flows in and out of the system, and due to freshwater dilution the water is brackish. The Corbridge Cave tidal sumps provide a window into the creek; the sea anenome Cerianthus lloydii has been found burrowing in thick mud on the floor and a few worms and other forms hang on where tidal currents keep mud from settling on the walls, but generally life is sparse. The shallow tidal pool in the west bay of the quarry, connected to the sea only by this underground creek, is in effect an isolated brackish lake. In the 20 years since it was quarried out, it has been colonised by various estuarine worms, molluscs and crustacea. Larger mobile marine animals are also present. In caves that open directly to the sea, many fish and crustaceans can be found, notably prawns, which like bats in a dry cave use it as a daytime retreat. Prawns regularly penetrate into Corbridge Cave; common eels have been seen in Corbridge Cave and in the tidal pool beyond it. The results of the first stage of the investigation of the Berry Head caves - their survey - are presented here. This atlas is concerned mainly with Berry Head itself, and surveys of all the significant caves within this area are included. A list of the minor sites is given at the end of the survey section. The area covered does not include any of the well known Berry Head Sea Caves since none of these are actually on the head: they are scattered along the coast between Berry Head and St Mary's Bay to the south. Since they are popularly associated with Berry Head, however, descriptions of them are given in a separate section at the back. Thus in total the area covered encompasses all the limestone area east of Brixham. The present publication is intended to cover all caves known up to July 1987: I would be very grateful for information about any errors, ommissions and new discoveries, so that the atlas can be kept up to date and as accurate as possible. ACCESS Apart from Ash Hole, the Berry Head Caves lie within Berry Head Country Park, and in November 1986 became a site of special scientific interest on account of the bat colony and other special features of the caves. Permission is necessary to visit all caves in the country park; intending visitors should contact the warden. At present permission can only normally be given to members of a recognised caving club. The following special CORBRIDGE CAVE, HOGBERRY CAVE, SHAKY CAVE, HIGHER SHAKY CAVE. Gated: the owner has specified that all parties must be led - please please give advance notice so that a leader can be arranged. SWEETWATER POT, BATHBERRY CAVE, SPAR CAVE. Acessible only by a 55m abseil down the quarry face: tackle 60m rope and long belays to dubious fence post on cliff tops (use more than one). SEA LEVEL CAVES. Most are accessible only at low tide and in calm water. A wetsuit is necessary, and remember there are strong currents round the headland. Anyone wishing to carry out exploratory or scientific work is requested to obtain permission before doing so, in order to avoid conflicts with conservation interests and those of other workers. Berry Head is first and foremost a nature reserve, and it is essential that the wishes of the warden and the owners (Torbay Borough Council) be respected if cavers wish to keep access to the caves. Ash Hole is privately owned, but there has been free access for many yaers and the currentsituation seems to be that no permission is needed. The sea caves south of Berry Head are similarly without access restrictions, though a boat is necesary to visit the caves on Oxley Head. The bird colony below Oxley Head is covered by an area of special protection, and no climbing on the cliffs is permitted between March 15th and July 31st: this includes Rock Dove cave which is thus innaccessible for this period. Anyone boating near nesting seabirds is asked to keep nois to an absolute minimum. Loud noises may frighten the birds and result in eggs and nestlings being knocked from the cliff ledges.

Animals utilize their environment across a range of scales, which is bounded by their extent, the broadest spatial area which organisms respond to their environment within their lifetime, and the spatial grain, the smallest area they respond to their environment (Kotlier and Wiens 1990). Within this range, organisms likely respond to their environment at a hierarchy of levels. Johnson (1980) recognizes four distinct levels of hierarchical habitat selection. At the very largest scale, first order selection, includes the entire area that an organism utilizes within its lifetime, and is also known as an organisms global home range or extent. In contrast, second order selection is an organisms local home range, or the area that it occupies within a unique ecosystem. This distinction is most apparent with migratory animals who utilize more than one distinct landscape for their survival (i.e. summer vs. winter feeding grounds), and much less so for organisms resident of one specific landscape for their entire life span. Third order selection is the selection of specific habitat patches within an ecosystem. For example, a Monarch butterfly would tend to select patches of milkweed within a prairie. And the lowest level, fourth order selection, involves the physical procurement of food within a selected patch, in our example, specific flowers within a milkweed patch, and is also known as grain. Realizing the importance of hierarchical habitat selection, it has become apparent that single-scale studies of animals responses to their environment may fail to adequately represent how that specific animal is responding to ecological parameter of interest, especially if they are not responding to the landscape at that scale (Holling 1992). The range of scales which an animal of interest is utilizing a landscape is important to determine prior to any further ecological investigation, as inappropriate scalar mismatch between organism and environment can lead to ambiguous or even deceptive conclusions. To do this, we compared the correlation coefficients of bird abundances for different functional groups (e.g. foraging guilds, natives vs. exotics) with vegetation cover, as a proxy for habitat, across a range of scales (from 100m to 10km). Theoretically, a unimodal (hump-shaped) relationship should exist for the correlation coefficients across a range of scales, under the assumption that vegetation cover is an adequate estimate of bird abundance. The peak of that relationship, if statistically significant, would represent the strongest correlation between habitat and bird abundance, and thus signifies the average third order selection unit for that group. A strong peak is expected for species directly dependent on vegetation for food (herbivores), a weaker peak for omnivores, and the weakest relationship for those species indirectly dependent on vegetation (insectivores). The regional distributional patterns of the varying bird functional groups was also estimated by utilizing interpolation techniques designed for avian censuses in urban systems. Exotic species were expected to be spatially aligned to the urban ecosystem, and native species tied to the desert ecosystem. Herbivores were expected to exist in higher densities were vegetation is greatest, which typically exists within the city and agricultural fields in arid ecosystems. The ongoing project (since October 2000) is documenting the abundance and distribution of birds in four habitats (51 sites): Urban (18) Desert (15) Riparian (11) and agricultural (7). The 40 non-riparian sites are a subset of the 200 CAP- LTER points. We are using point counts to survey birds four times a year (January, April, July and October). During each session each point is visited by three birders who count all birds seen or heard for 15 minutes. Our goal is to study how different land-use fo... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-cap%2F394%2F7 for complete metadata about this dataset.

A water right is a legal right to use surface or ground water under the Alaska Water Use Act (AS 46.15). A water right allows a specific amount of water from a specific water source to be diverted, impounded, or withdrawn for a specific use. When a water right is granted, it becomes appurtenant to the land where the water is being used for as long as the water is used. If the land is sold, the water right transfers with the land to the new owner, unless the Department of Natural Resources (DNR) approves its separation from the land. In Alaska, because water wherever it naturally occurs is a common property resource, landowners do not have automatic rights to ground water or surface water. For example, if a farmer has a creek running through his property, he will need a water right to authorize his use of a significant amount of water. Using water without a permit or certificate does not give the user a legal right to use the water. This shape file characterizes the geographic representation of point locations within the State of Alaska contained by the Surface Water Rights category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction. Each feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://www.dnr.state.ak.us/las/LASMenu.cfm Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

The Most Probable Overland Flow Pathway dataset is a polyline GIS vector dataset that describes the likely flow routes of water along with potential accumulations of diffuse pollution and soil erosion features over the land. It is a complete network for the entire country (England) produced from a hydro-enforced LIDAR 1-metre resolution digital terrain model (bare earth DTM) produced from the 2022 LIDAR Composite 1m Digital Terrain Model. Extensive processing on the data using auxiliary datasets (Selected OS Water Network, OS MasterMap features as well as some manual intervention) has resulted in a hydro-enforced DTM that significantly reduces the amount of non-real-world obstructions in the DTM. Although it does not consider infiltration potential of different land surfaces and soil types, it is instructive in broadly identifying potential problem areas in the landscape. The flow network is based upon theoretical one-hectare flow accumulations, meaning that any point along a network feature is likely to have a minimum of one-hectare of land potentially contributing to it. Each segment is attributed with an estimate of the mean slope along it. The product is comprised of 3 vector datasets; Probable Overland Flow Pathways, Detailed Watershed and Ponding and Errors. Where Flow Direction Grids have been derived, the D8 option was applied. All processing was carried out using ARCGIS Pro’s Spatial Analyst Hydrology tools. Outlined below is a description of each of the feature class. Probable Overland Flow Pathways The Probable Overland Flow Pathways layer is a polyline vector dataset that describes the probable locations accumulation of water over the Earth’s surface where it is assumed that there is no absorption of water through the soil. Every point along each of the features predicts an uphill contribution of a minimum of 1 hectare of land. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Every effort has been used to digitally unblock real-world drainage features; however, some blockages remain (e.g. culverts and bridges. In these places the flow pathways should be disregarded. The Ponding field can be used to identify these erroneous pathways. They are flagged in the Ponding field with a “1”. Flow pathways are also attributed with a mean slope value which is calculated from the Length and the difference of the start and end point elevations. The maximum uphill flow accumulation area is also indicated for each flow pathway feature. Detailed Watersheds The Detailed Watersheds layer is a polygon vector dataset that describes theoretical catchment boundaries that have been derived from pour points extracted from every junction or node of a 1km2 Flow Accumulation dataset. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Ponding Errors The Ponding and Errors layer is a polygon vector dataset that describes the presence of depressions in the landscape after the hydro-enforcing routine has been applied to the Digital Terrain Model. The Type field indicates whether the feature is Off-Line or On-Line. Off-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. On-line features do not intersect with watercourses and are more likely to be depressions in the landscape where standing water may accumulate. Only features of greater than 100m2 with a depth of greater than 20cm have been included. The layer was derived by filling the hydro-enforced DTM then subtracting the hydro-enforced DTM from the filled hydro-enforced DTM. Please use with caution in very flat areas and areas with highly modified drainage systems (e.g. fenlands of East Anglia and Somerset Levels). There will occasionally be errors associated with bridges, viaducts and culverts that were unable to be resolved with the hydro-enforcement process. Attribution statement: © Environment Agency copyright and/or database right 2023. All rights reserved.

This dataset represents water quality data collected from ditch and streams in a Minnesota Agricultural Watershed, High Island Creek. Data were collected from an inflatable raft with high spatial resolution resulting in water quality maps for selected portions of the watershed. These data were interpreted within the ecological context of spatial leverage to characterize watershed influences on nitrogen and carbon transport or removal from the stream. These data were used to prepare a manuscript for publication in the journal: Water Resources Research. The abstract and plain language summary from that paper is copied below.AbstractMany agricultural landscapes have undergone significant modifications to drain farmland and improve crop productivity. Subsurface field drainage, ditching and channelization of streams limit opportunities for biogeochemical processing of carbon and nutrients within the channel network. In this study, we used spatially rich water quality data collected from two contrasting regions of an agricultural watershed in south-central Minnesota, USA to assess how watershed features, such as channelization, tile drainage, and presence of lakes or wetlands, influence biogeochemical processing of nitrate (NO3-) and dissolved organic carbon (DOC). In the channelized upstream region, land use is predominantly agricultural (> 92%) with subsurface tile drainage commonly discharging directly to the stream channel. Further downstream, the channel is more natural with increasing lakes and wetlands, including riparian wetlands. We used the concept of reach leverage to interpret biogeochemical behavior (i.e., source vs. sink) in each region of the watershed. Results indicate variability in biogeochemical behavior between the distinct watershed regions, suggesting that channel characteristics and the presence of lentic waters play a role in regulating biogeochemical processing. The upstream, channelized region acts primarily as a conservative transporter or small source of both NO3- and DOC across sampling dates. In contrast, the lentic-influenced region exhibited shifts between source and sink behavior over time, especially for NO3-, influenced by factors such as hydrologic connectivity and discharge. These findings highlight the value of collecting spatially resolved data to enhance our understanding of biogeochemical processing which may be useful to inform effective management and conservation strategies.Plain Language SummaryMany farmlands have been altered to drain water and increase crop production. These changes often involve straightening natural stream channels, which reduces their ability to use nutrients and carbon. In this study, we collected detailed water quality data from two different areas of an agricultural watershed in south-central Minnesota to see how features like straightened channels, drainage systems, and the presence of lakes or wetlands affect the processing of nitrate (NO3-) and dissolved organic carbon (DOC). The upstream area is mostly farmland with drainage systems that empty directly into the stream, while the downstream area has more lakes and wetlands, creating a more natural stream environment. We used a method called reach leverage to understand whether areas of the watershed were sources of NO3- and DOC, or if they removed them. Our results showed differences in nutrient processing between the two areas. The upstream, straightened region mainly transported or slightly increased NO3- and DOC, while the downstream, wetland-rich region alternated between acting as a source and a sink for NO3- depending on water flow and other factors. These findings highlight the importance of collecting detailed, location-specific data to understand nutrient processing and for developing better land and water management strategies.

This record is for Approval for Access product AfA268. The Environment Agency is responsible for licensing the abstraction of water in England and Wales. Abstraction licences set out how much water can be abstracted and for what purpose (licensed abstractions). Licence holders may also be required to measure their abstractions and submit how much water has actually been abstracted (actual abstractions). ABSTAT does not to attempt to estimate unlicensed abstractions. Abstraction Statistics (ABSTAT) provides details of licensed abstractions and estimates of actual abstractions on the basis of an agreed set of purpose categories and abstraction source types for each calendar year from 2000. It also supplies total number of licences issued for each purpose category. Tables in general are supplied with regional breakdowns. ABSTAT is updated each November. Abstracted quantities are measured in megalitres per day (ML/day). This data is also available on the Defra website. On request information can be provided for the period from 1995 to 2010 INFORMATION WARNING 1. Please read through the summary note 'ABSTAT_summary_v10_external version.pdf' before using ABSTAT (you will need Adobe Acrobat) 2. Under the Water Act 2003 abstraction of up to 20m3/day became exempt from the requirement to hold a licence from 1 April 2005. As a result over 22,000 licences were deregulated (mainly for agricultural or private water supply purposes). 3. Return requirements were changed from 01/04/2008 whereby licences that authorise under 100m3/day are no longer asked to submit records of abstraction to the Environment Agency. This may have had a minimal impact on some reported purposes e.g. agriculture, private water supply and other. 4. From 01/04/2008 return requirements were standardised across England & Wales and the majority of returns are now requested on financial years. To align previous reporting practices two return requests were made for 2008. One at the end of the period January 2008 to March 2008 and a second at the end of period April 2008 to March 2009. This may have had an effect on underestimating actuals whereby returns may have been received for only part of the calendar year. 5. Tables 3_20 & 3_21 do not include impoundment licences or transfer type licences. A licence may authorise abstraction from a single point for a single purpose in some instances a licence can authorise abstraction from multiple points and/or for multiple purposes. ABSTAT is a purpose driven report, so where a licence authorises abstraction for more than one purpose the licence is included in the count for each use category. This will result in an overestimation of the actual number of abstraction licences in force as reported by ABSTAT. 6. Spray irrigation is very sensitive to prevailing weather conditions. 7. The Electricity Supply category includes hydropower licences. 8. Table 3_20: data labels corrected in ABSTAT2010 update to calendar year (from financial year in previous ABSTAT updates). Change applies from 2000 for 'number of licences in force' and from 2008 for 'number of new licences determined'. Table 3-21: data label corrected to calendar year in ABSTAT2010 update (from financial year in previous ABSTAT updates). 9. From 1 April 2011 EA Thames and EA Southern merged to form EA South East. The two regions are still shown separately as this is the basis of the WR charges scheme, this is reflected by the underlying reference information used to prepare ABSTAT. 10. ABSTAT is a purpose driven report, so where a licence authorises abstraction for more than one purpose the licence is included in the count for each use category. This will result in an overestimation of the actual number of abstraction licences in force as reported by ABSTAT. 11. Reference to financial year means 1 April to 31 March inclusive. Attribution statement: © Environment Agency copyright and/or database right 2019. All rights reserved.

In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recovery under an unknown low rank, etc. Despite the ease of obtaining a point estimate via optimization, it is much more challenging to quantify their uncertainty. In the Bayesian framework, a major difficulty is that if assigning the prior associated with a p-dimensional measure, then there is zero posterior probability on any lower-dimensional subset with dimension d < p. To avoid this caveat, one needs to choose another dimension-selection prior on d, which often involves a highly combinatorial problem. To significantly reduce the modeling burden, we propose a new generative process for the prior: starting from a continuous random variable such as multivariate Gaussian, we transform it into a varying-dimensional space using the proximal mapping. This leads to a large class of new Bayesian models that can directly exploit the popular frequentist regularizations and their algorithms, such as the nuclear norm penalty and the alternating direction method of multipliers, while providing a principled and probabilistic uncertainty estimation. We show that this framework is well justified in the geometric measure theory, and enjoys a convenient posterior computation via the standard Hamiltonian Monte Carlo. We demonstrate its use in the analysis of the dynamic flow network data. Supplementary materials for this article are available online.