You'll always know which way is north with this attractive at-home compass activity.

We present several input sample files for the execution of various Nextflow pipelines developed by the Cardiovascular Proteomics Lab/Proteomics Unit at the National Centre for Cardiovascular Research (CNIC, https://www.cnic.es).

The nf-PTM-compass enhances the identification and quantification of Post-Translational Modifications (PTMs) (https://github.com/CNIC-Proteomics/nf-PTM-compass).

The available sample files are as follows:

• nf-PTM-compass_Heteroplasmic_Heart.zip: Heart tissue. Input files for nf-PTM-compass, derived from the study by Bagwan N, Bonzon-Kulichenko E, Calvo E, et al. (*), with results from an open search conducted using nf-SearchEngine results.
• nf-PTM-compass_Heteroplasmic_Liver.zip: Liver tissue. Input files for nf-PTM-compass, derived from the same study (*), with results from an open search conducted using nf-SearchEngine results.
• nf-PTM-compass_Heteroplasmic_Muscle.zip: Mucle tissue. Input files for nf-PTM-compass, derived from the same study (*), with results from an open search conducted using nf-SearchEngine results.

(*) Bagwan N, Bonzon-Kulichenko E, Calvo E, et al. Comprehensive Quantification of the Modified Proteome Reveals Oxidative Heart Damage in Mitochondrial Heteroplasmy. Cell Reports. 2018;23(12):3685-3697.e4. doi:10.1016/j.celrep.2018.05.080

See below for direct links to commonly requested dataRegional Centerline: (Updated monthly, collectors and up)Access Regional Centerline HereRail:Mainline railAccess Main Rail HereRail Spurs and Sidings:All existing rail (created using 2016 COMPASS imagery)Access All Rail HereCounty Boundaries:Updated as neededAccess County Boundaries HereCity Limits: Updated on a periodic basisAccess City Limits HereImpact Areas:Updated as neededAccess Impact Area Data HereDemographic Areas:Subarea geographies that have common characteristics; made up of Traffic Analysis Zones, the unit of geography that demographics are calculated at COMPASS. Access Demographic Areas HereVRT Routes: (Updated as changes are made)Access VRT Route Data HereVRT Stops: (Updated as changes are made)Access VRT Stop Data HereFiscal Impact Subareas:Updated as neededAccess the Fiscal Impact Tool Subareas HereUrbanized _2020 Census Ada and Canyon Counties:Created after the release of the 2020 Census. Access the 2020 Census Urban Boundaries HereSubdivisions:Updated on a periodic basisAccess Subdivisions HereIntersection Nodes with Type:Updated on a periodic basisAccess the Intersection Nodes HereRegional Comprehensive Plans: (Updated as needed)Use the RegionalGeneral field. Access Regional Comprehensive Plans HereCurrent Land Use: (Updated Yearly)Use the CurrentUse field.Access Current Land Use Here

Traces of signal strength of 802.11 APs for the COMPASS positioning system.Note: This dataset has multiple versions. The dataset file names of the data associated with this version are listed below, under the 'Traceset' heading and can be downloaded under 'Dataset Files' on the right-hand side of the page.COMPASS is a positioning system based on 802.11 and digital compasses. We apply an two-stage fingerprinting approach: In the training phase, we sample the signal strength of neighboring access points for selected orientations at each reference point and store the data in a database. During the positioning phase, the orientation of the user is utilized to preselect a subset of the training data and based on this data compute her position.last modified :2008-04-16release date :2008-04-11date/time of measurement start :2006-02-11date/time of measurement end :2006-10-14collection environment :Positioning systems are one of the key elements required by location-based services. We design and implement a positioning system called COMPASS which is based on 802.11-compliant network infrastructure and digital compasses. On the mobile device, COMPASS samples the signal strength values of different access points in its communication range and utilizes the orientation of the user to preselect a subset of the training data. The remaining training data is used by a probabilistic positioning algorithm to determine the position of the user. While prior systems show limited accuracy due to blocking effects caused by the human body, we apply digital compasses to detect the orientations of the users so that they can deal with these blocking effects. After a short period of training the COMPASS system achieves an average error distance of less than 1.65 meters in the experimental environment of 312 square meters.network configuration :The test environment is equipped with five Linksys / Cisco WRT54GS and four Lancom L-54g access points. All access points support 802.11b and 802.11g. One Lancom and all Linksys access points are located on the same floor as our testing area whereas three Lancom access point are located in other places inside the building. The exact position of the access points located inside the testing area is marked by squares in the floor plan (see the download link below).data collection methodology :We deployed our positioning system in the hallway of an office building on the campus of the University of Mannheim. The operation area is nearly 15 meters in width and 36 meters in length, covering an area of approximately 312 square meters. The floor plan of the testing area is shown in the floor plan figure (see the download link below). The large hallway in the left part of the map is connected by two narrow hallways that are separated by rooms such as archives and a kitchen. We marked the floor plan (see the download link below) with markers depicting the grid of the reference points (light-colored dots) and the online measurement points (dark dots). The access points are marked by squares. As a client, we used a Lucent Orinco Silver PCMCIA network card supporting 802.11b. We collected the signal strength samples on an IBMThinkpad R51 running Linux kernel 2.6.13 and Wireless Tools 28pre. To obtain the orientation of the user we used the Silicon Laboratories C8051F350 Digital Compass Reference Design Board. This device provides a USB-to-Serial bridge to access the data and is powered by the USB electricity supply. We calibrated the compass in the middle of the operation area. In a closer area around the calibration point we measured a variation of 1 degree. However, variations up to 23 degree were rarely detected at a few points of the testing area. These measurement errors occured always close to electromagnetic objects such as high-voltage power lines and electronic devices.Tracesetmannheim/compass/signalstrengthA traceset of signal strength collected from 802.11 APs for the COMPASS positioning system.files: offline.tar.gz, online.tar.gzdescription: A traceset of signal strength collected from 802.11 APs for the location estimation used by the COMPASS positioning system.measurement purpose: Location-aware Computing, Positioning Systemsmethodology: The grid of reference points applied to the operation area includes 166 points with a spacing of 1 meter (see the light-colored dots in the floorplan figure). During the offline phase, the signal strength was measured at reference points for different orientations. We then randomly selected 60 coordinates and orientations for the online phase.last modified: 2006-11-14dataname: mannheim/compass/signalstrengthversion: 20060913change: the initial versionrelease date: 2006-09-13date/time of measurement start: 2006-02-11date/time of measurement end: 2006-03-09mannheim/compass/signalstrength Tracesoffline: A trace of signal strength values from 802.11 APs measured at reference points for different orientations.configuration: During the offline phase, the signal strength was measured at reference points for different orientations. We collected 110 signal strength measurements at each reference point and for each orientation. This leads to 146,080 measurements for the offline phase. We spent over 10 hours to collect all the data.format: (format of trace data)t="Timestamp"; id="MACofScanDevice"; pos="RealPosition"; degree="orientation"; "MACofResponse1"="SignalStrengthValue","Frequency","Mode"; ... "MACofResponseN"="SignalStrengthValue","Frequency","Mode" t: timestamp in milliseconds since midnight, January 1, 1970 UTC id: MAC address of the scanning device pos: the physical coordinate of the scanning device degree: orientation of the user carrying the scanning device in degrees MAC: MAC address of a responding peer (e.g. an access point or a device in adhoc mode) with the corresponding values for signal strength in dBm, the channel frequency and its mode (access point = 3, device in adhoc mode = 1)description: A trace of signal strength values from 802.11 APs measured at reference points for different orientations for the offline phase of the COMPASS positioning system.last modified: 2006-11-14dataname: mannheim/compass/signalstrength/offlineversion: 20060913change: the initial versionrelease date: 2006-09-13date/time of measurement start: 2006-02-11date/time of measurement end: 2006-03-09url: /download/mannheim/compass/offline.tar.gzonline: A trace of signal strength, which is derived from mannheim/compass/signalstrength/offline.configuration: We randomly selected 60 coordinates and orientations for the online phase. The only condition to select a point inside the testing area as an online set point is that it is surrounded by four reference points. Again, we collected 110 signal strength measurements for each online set point, leading to 6,600 measurements in total.format: (format of trace data) t="Timestamp"; id="MACofScanDevice"; pos="RealPosition"; degree="orientation"; "MACofResponse1"="SignalStrengthValue","Frequency","Mode"; ... "MACofResponseN"="SignalStrengthValue","Frequency","Mode" t: timestamp in milliseconds since midnight, January 1, 1970 UTC id: MAC address of the scanning device pos: the physical coordinate of the scanning device degree: orientation of the user carrying the scanning device in degrees MAC: MAC address of a responding peer (e.g. an access point or a device in adhoc mode) with the corresponding values for signal strength in dBm, the channel frequency and its mode (access point = 3, device in adhoc mode = 1)description: A trace of signal strength, which is derived from mannheim/compass/signalstrength/offline for online phase of the COMPASS positioning system.last modified: 2006-11-14dataname: mannheim/compass/signalstrength/onlineversion: 20060913change: the initial versionrelease date: 2006-09-29date/time of measurement start: 2006-02-11date/time of measurement end: 2006-03-09url: /download/mannheim/compass/online.tar.gzmannheim/compass/802.11A traceset of signal strength collected from 802.11 APs for the COMPASS positioning system.description: A traceset of signal strength collected from 802.11 APs for the location estimation used by the COMPASS positioning system.measurement purpose: Location-aware Computing, Positioning Systemsmethodology: 1. Local Test Environment We deployed the positioning system on the second floor of an office building on the campus of the University of Mannheim. The operation area is nearly 15 meters in width and 36 meters in length, covering an area of approximately 312 square meters. The floor plan of the operation area is shown in [Figure: floor plan for mannheim/compass/802.11]. The large hallway in the left part of the map is connected by two narrow hallways that are separated by rooms such as a copier room, an archive and a kitchen. The rooms depicted on both sides of the narrow hallways are mainly used as offices, and due to access restrictions they could not be included into the operation area. 2. Hardware and Software Setup Initially, the test environment was covered by one Linksys / Cisco WRT54GS and two enterasys RBT-4102-EU access points administered by the computer center of our university. We additionally installed 11 access points: Two D-Link DWL-G700AP, three NETGEAR WG102, and six Linksys / Cisco WRT54G access points. All access points support 802.11b and 802.11g. Except of one enterasys access point, all access points are located on the same floor as our operation area. This particular enterasys access point is placed on a lower floor, however, it covers the operation area completely. The position of this access point is marked by an orange ring and the positions of the other access points are marked by orange circles (see [Figure: floor plan for mannheim/compass/802.11]). As a client, we used a Lucent Orinoco Silver PCMCIA network card supporting 802.11b. This card was plugged into an IBM Thinkpad R51 running Linux kernel 2.6.13 and Wireless Tools 28pre. To collect signal strength samples, we

EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:

'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'

Successfully integrating transcriptomic experiments is a challenging task with the ultimate goal of analyzing gene expression data in the broader context of all available measurements, all from a single point of access. In its second major release VESPUCCI, the integrated database of gene expression data for grapevine, has been updated to be FAIR-compliant, employing standards and created with open-source technologies. It includes all public grapevine gene expression experiments from both microarray and RNA-seq platforms. Transcriptomic data can be accessed in multiple ways through the newly developed COMPASS GraphQL interface, while the expression values are normalized using different methodologies to flexibly satisfy different analysis requirements. Sample annotations are manually curated and use standard formats and ontologies. The updated version of VESPUCCI provides easy querying and analyzing of integrated grapevine gene expression (meta)data and can be seamlessly embedded in any analysis workflow or tools. VESPUCCI is freely accessible and offers several ways of interaction, depending on the specific goals and purposes and/or user expertise; an overview can be found at https://vespucci.readthedocs.io/.

Open loop - Light offFlight experiments with open loop period (light off), prior to closed-loop flight. (Fig. 7)open_loop_light_off_0.csvOpen Loop - Light OnFlight experiments with open loop period (light on) prior to closed-loop flight. (Fig. 7)open_loop_light_on_0.csvPerturbed Paired (1/2)First of 2 data files with data from perturbed, paired flight experiments. (Fig. 3)perturbed_paired_0.csvPerturbed Paired (2/2)Second of 2 data files with perturbed, paired flight experiments. (Fig. 3)perturbed_paired_1.csvUnperturbed, paired (1/3)First of 3 data files from unperturbed, paired experiments. (Fig. 3)unperturbed_paired_0.csvUnperturbed, paired (2/3)Second of 3 data files from unperturbed, paired flight experiments. (Fig. 3)unperturbed_paired_1.csvUnperturbed, paired (3/3)Third of 3 data files with unperturbed, paired flight experiments. (Fig. 3)unperturbed_paired_2.csvCircular PolarizerFlight data with circular polarizer, control stimulus.circular_polarizer_0.csvLinear Polarizer, 365 nm...

E-Compass Market size was valued at USD 2.9 Billion in 2023 and is projected to reach USD 3.08 Billion by 2030, growing at a CAGR of 15.3% from 2024 to 2030.

Global E-Compass Market Drivers

Proliferation of Smartphones and Wearable Technology: One of the key factors propelling the E-compass market is the extensive use of smartphones and wearable technology. To give consumers access to augmented reality, location-based services, and navigation, these gadgets frequently include E-compass capability. The market for integrated E-compass solutions is growing along with the demand for feature-rich smartphones and wearables.

Growth in Automotive Applications: Advanced driver assistance systems (ADAS), vehicle tracking, and navigation are just a few of the automotive applications that are using e-compasses more and more. They improve navigation accuracy and make technologies like adaptive cruise control and lane departure warning possible by providing correct heading information regardless of the movement of the vehicle. As technology becomes more integrated into cars, there is an increasing need for E-compasses in this industry.

Demand from the Aerospace and Defense Sector: E-compass technology is used in the aerospace and defense sectors for a number of applications, such as soldier navigation systems, unmanned aerial vehicles (UAVs), and aircraft navigation. In a variety of settings, such as places where GPS reception is restricted or where electromagnetic interference is common, e-compasses provide accurate orientation sensing capabilities. E-compass technologies continue to be in high demand as defense modernization initiatives and commercial aerospace advancements persist.

The emergence of the Internet of Things (IoT) and connected devices: E-compasses and other sensor technologies are essential to the IoT ecosystem’s industrial, smart home automation, and asset tracking uses. E-compasses improve the functioning of Internet of Things devices by enabling precise position sensing and orientation tracking, opening up new use cases. The market for IoT devices with inbuilt E-compasses is growing as it spreads throughout different industries.

Improvements in MEMS Technology: By permitting shrinking, low power consumption, and cost-effectiveness, microelectromechanical systems (MEMS) technology has greatly enhanced E-compass capabilities. MEMS-based E-compass sensors are ideal for battery-operated portable devices and Internet of Things applications because they provide great precision and dependability at low power consumption. Continuous developments in MEMS technology lead to enhanced affordability and performance of E-compasses, which further stimulates market expansion.

Integration with Virtual and Augmented Reality: E-compasses are essential for AR and VR applications because they offer precise orientation tracking for immersive experiences. E-compass technology is employed by AR smart glasses, VR headsets, and mixed reality devices to align virtual content with the user’s physical environment, hence improving realism and user interaction. It is anticipated that demand for E-compass integration will increase as AR and VR technologies continue to develop and acquire popularity across industries.

Outdoor Recreation and Sports Activities: E-compasses are becoming more and more important for navigation, route planning, and fitness tracking for hikers, outdoor lovers, and athletes. Users can access location-based features and real-time navigation information using smartwatches, fitness trackers, and handheld GPS devices that have e-compasses integrated into them. The need for E-compass solutions designed for these areas is fueled by the rising popularity of sports and outdoor recreation.

BackgroundUsing primary human papillomavirus (HPV) testing for cervical screening increases detection of high-grade cervical intraepithelial neoplastic lesions and invasive cancer (cervical intraepithelial neoplasia grade 2+ [CIN2+]) compared to cytology, but no evaluation has been conducted in a population previously offered HPV vaccination. We aimed to assess colposcopy referral and CIN2+ detection rates for HPV-screened versus cytology-screened women in Australia’s HPV-vaccinated population (by 2014, resident women ≤33 years had been age-eligible for HPV vaccination, with 3-dose uptake across age cohorts being about 50%–77%).Methods and findingsCompass is an open-label randomised trial of 5-yearly HPV screening versus 2.5-yearly liquid-based cytology (LBC) screening. In the first phase, consenting women aged 25–64 years presenting for routine screening at 47 primary practices in Victoria, Australia, provided a cervical sample and were randomised at a central laboratory at a 1:2:2 allocation to (i) image-read LBC screening with HPV triage of low-grade cytology (‘LBC screening’), (ii) HPV screening with those HPV16/18 positive referred to colposcopy and with LBC triage for other oncogenic (OHR) types (‘HPV+LBC triage’), or (iii) HPV screening with those HPV16/18 positive referred to colposcopy and with dual-stained cytology triage for OHR types (‘HPV+DS triage’). A total of 5,006 eligible women were recruited from 29 October 2013 to 7 November 2014 (recruitment rate 58%); of these, 22% were in the group age-eligible for vaccination. Data on 4,995 participants were analysed after 11 withdrawals; 998 were assigned to, and 995 analysed (99.7%) in, the LBC-screened group; 1,996 assigned to and 1,992 analysed (99.8%) in the HPV+LBC triage group; and 2,012 assigned to and 2,008 analysed (99.8%) in the HPV+DS triage group. No serious trial-related adverse events were reported. The main outcomes were colposcopy referral and detected CIN2+ rates at baseline screening, assessed on an intention-to-treat basis after follow-up of the subgroup of triage-negative women in each arm referred to 12 months of surveillance, and after a further 6 months of follow-up for histological outcomes (dataset closed 31 August 2016). Analysis was adjusted for whether women had been age-eligible for HPV vaccination or not. For the LBC-screened group, the overall referral and detected CIN2+ rates were 27/995 (2.7% [95% CI 1.8%–3.9%]) and 1/995 (0.1% [95% CI 0.0%–0.6%]), respectively; for HPV+LBC triage, these were 75/1,992 (3.8% [95% CI 3.0%–4.7%]) and 20/1,992 (1.0% [95% CI 0.6%–1.5%]); and for HPV+DS triage, these were 79/2,008 (3.9% [95% CI 3.1%–4.9%]) and 24/2,008 (1.2% [95% CI 0.8%–1.6%]) (p = 0.09 for difference in referral rate in LBC versus all HPV-screened women; p = 0.003 for difference in CIN2+ detection rate in LBC versus all HPV-screened women, with p = 0.62 between HPV screening groups). Limitations include that the study population involved a relatively low risk group in a previously well-screened and treated population, that individual women’s vaccination status was unknown, and that long-term follow-up data on disease detection in screen-negative women are not yet available.ConclusionsIn this study, primary HPV screening was associated with significantly increased detection of high-grade precancerous cervical lesions compared to cytology, in a population where high vaccine uptake was reported in women aged 33 years or younger who were offered vaccination. It had been predicted that increased disease detection might be associated with a transient increase in colposcopy referral rates in the first round of HPV screening, possibly dampened by HPV vaccine effect; in this study, although the point estimates for referral rates in women in each HPV-screened group were 41%–44% higher than in cytology-screened women, the difference in referral rate between cytology- and HPV-screened women was not significant. These findings provide initial support for the implementation of primary HPV screening in vaccinated populations.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12613001207707

This is the version 1-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. This dataset will be updated quarterly with new data for the duration of the project This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.

A data layer created from COMPASS member agency plans. This data is under review, and is the best available pending updates when plans are updated.

Category Gallery presents content from your group with a variety of ways to filter and sort your items with the options to apply your organization's content categories or categories from your group. Filters in the app are preserved in the URL, allowing viewers to share app with filters in their current state. Choose to show information about items on each item card, including description, owner, date created, date modified, and view count.Examples:Create a category based atlas focused on a topic or area of interestPresent a collection of maps and apps for citizens to learn about their cityProvide the option to filter, search, and sort content within a groupData RequirementsThis app has no data requirements.Key App CapabilitiesFilter options - Filter items in the app by category, item type, date created, date modified, sharing settings, status, and tagsItem card display - Choose the details displayed on content items, including item type, owner name, date created, date modified, view count, link to item page, and full summaryMap options - Enable map tools to appear when viewers open maps in the app, including compass, basemap toggle, and find current locationProvide a title and optionally customize the contents of the header via HTMLHome, Zoom Controls, Legend, Layer List, SearchSupportabilityThis web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Ethical principles are fundamental for the long-term sustainable growth of AI/ML; however, recent research highlights that many projects have yet to fully integrate these guidelines. This research aims to assess the adoption of ethical principles in AI/ML within the software development space by analyzing pull requests from 28 AI/ML GitHub projects. Motivated by the observation that only about 3.4% of all pull requests contained ethical keywords - indicating a lack of explicit attention to ethical considerations - we manually
labeled 400 randomly selected pull requests according to the seven EU ethical guidelines. Addressing the challenge of scalability and consistency in manual labeling, we inves- tigated the use of a zero-shot large language model (LLM), OpenAI’s GPT-4o, to automatically detect ethical AI principles in pull requests. Various prompts and temperature settings were tested to enhance model performance and interpretability. Our findings demonstrate that GPT-4o has the potential to support ethical compliance in software development. Looking ahead, we envision automating the scanning of code changes for ethical concerns, similar to vulnerability detection models. Such a tool would flag high-risk pull requests for ethical review, aiding AI risk assessment in open-source projects, and support the automatic generation of an AI Bill of Materials (AI BOM).

The process of resonance assignment is fundamental to most NMR studies of protein structure and dynamics. Unfortunately, the manual assignment of residues is tedious and time-consuming, and can represent a significant bottleneck for further characterization. Furthermore, while automated approaches have been developed, they are often limited in their accuracy, particularly for larger proteins. Here, we address this by introducing the software COMPASS, which, by combining automated resonance assignment with manual intervention, is able to achieve accuracy approaching that from manual assignments at greatly accelerated speeds. Moreover, by including the option to compensate for isotope shift effects in deuterated proteins, COMPASS is far more accurate for larger proteins than existing automated methods. COMPASS is an open-source project licensed under GNU General Public License and is available for download from http://www.liu.se/forskning/foass/tidigare-foass/patrik-lundstrom/software?l=en. Source code and binaries for Linux, Mac OS X and Microsoft Windows are available.

Please note, this dataset is not suitable for identifying whether an individual property will flood. GIS layer showing the dominant flow direction of flooding from surface water, at maximum speed, that could result from a flood with a 0.1% chance of happening in any given year. The flood flow direction is resampled from a 2m grid to a 25m grid and is grouped into 8 bands (compass directions). This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Information Warnings:Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence.Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right. Find out more or download the dataset at environment.data.go.uk.

These boundaries have been determined by CDA mostly though AAA, and Compass maps and corroborated via assessor ownership data. Occational updates use ownership data to pinpoint new purchases of land by owners of currently tracted features.

Flat elements of the cultural-historical main structure for the province of Drenthe concerning selected esvillages. Characteristic structure of open spaces and brinks, scattered buildings, goorns and views to ash and stream valley.

description: Digital data from VG09-7 Van Hoesen, J., 2009, Surficial Geologic Map of Rutland, Vermont: Vermont Geological Survey Open-File Report VG09-7, 9 plates, scale 1:24,000. Data may include surficial geologic contacts, isopach contours lines, bedrock outcrop polygons, bedrock geologic contacts, hydrogeologic units and more. The surficial geologic materials data at a scale of 1:24,000 depict types of unconsolidated surficial and glacial materials overlying bedrock in Vermont. Data is created by mapping on the ground using standard geologic pace and compass techniques and/or GPS on a USGS 1:24000 topographic base map. The materials data is selected from the Vermont Geological Survey Open File Report (OFR) publication (https://dec.vermont.gov/geological-survey/publication-gis/ofr). The OFR contains more complete descriptions of map units, cross-sections, isopach maps and other information that may not be included in this digital data set.; abstract: Digital data from VG09-7 Van Hoesen, J., 2009, Surficial Geologic Map of Rutland, Vermont: Vermont Geological Survey Open-File Report VG09-7, 9 plates, scale 1:24,000. Data may include surficial geologic contacts, isopach contours lines, bedrock outcrop polygons, bedrock geologic contacts, hydrogeologic units and more. The surficial geologic materials data at a scale of 1:24,000 depict types of unconsolidated surficial and glacial materials overlying bedrock in Vermont. Data is created by mapping on the ground using standard geologic pace and compass techniques and/or GPS on a USGS 1:24000 topographic base map. The materials data is selected from the Vermont Geological Survey Open File Report (OFR) publication (https://dec.vermont.gov/geological-survey/publication-gis/ofr). The OFR contains more complete descriptions of map units, cross-sections, isopach maps and other information that may not be included in this digital data set.

Digital Data from VG2017-2 Thompson, P. J., and Thompson, T. B., 2017, Bedrock Geologic Map of the Mount Mansfield 7.5 Minute Quadrangle, Vermont: VGS Open-File Report VG2017-2, Plate 1, scale 1:24000. The bedrock geologic map data at a scale of 1:24,000 depicts types of bedrock underlying unconsolidated materials in Vermont. Data is created by mapping on the ground using standard geologic pace and compass techniques and/or GPS on a USGS 1:24000 topographic base map. Data may be organized by town, quadrangle or watershed. Each data bundle may include point, line and polygon data and some or all of the following: 1) contacts (lithogic contacts), 2) fault_brittle, 3) fault_ductile, 4) fault_thrust, 5) fault_bed_plane (bedding plane thrust), 6) bedding, 7) bedding_graded (graded bedding) 8) bedding_overturn (overturned bedding), 9) bedding_select (selected points for published map), 10) foliation_n1, n2, n3 etc (foliation data), 11) outcrop (exposed outcrops), 12) field_station (outcrop and data collection point), 13) fold_axis, 14) axial_plane, 15) lamprophyre, 16) water_well_log (water well driller information), 16) linear_int (intersection lineation), 17) linear_str (stretching lineation) 18) x_section_line (line of cross-section), and photolinear (lineaments identified from air photos). Other feature classes may be included with each data bundle. (https://dec.vermont.gov/geological-survey/publication-gis/ofr)

Used by Pozi for Community Compass open data.