OSU_SnowCourse Summary: Manual snow course observations were collected over WY 2012-2014 from four paired forest-open sites chosen to span a broad elevation range. Study sites were located in the upper McKenzie (McK) River watershed, approximately 100 km east of Corvallis, Oregon, on the western slope of the Cascade Range and in the Middle Fork Willamette (MFW) watershed, located to the south of the McKenzie. The sites were designated based on elevation, with a range of 1110-1480 m. Distributed snow depth and snow water equivalent (SWE) observations were collected via monthly manual snow courses from 1 November through 1 April and bi-weekly thereafter. Snow courses spanned 500 m of forested terrain and 500 m of adjacent open terrain. Snow depth observations were collected approximately every 10 m and SWE was measured every 100 m along the snow courses with a federal snow sampler. These data are raw observations and have not been quality controlled in any way. Distance along the transect was estimated in the field. OSU_SnowDepth Summary: 10-minute snow depth observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meterological stations were located in the approximate center of each forest or open snow course transect. These data have undergone basic quality control. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN flags for missing data to NA, and added site attributes such as site name and cover. We replaced positive values with NA, since snow depth values in raw data are negative (i.e., flipped, with some correction to use the height of the sensor as zero). Thus, positive snow depth values in the raw data equal negative snow depth values. Second, the sign of the data was switched to make them positive. Then, the smooth.m (MATLAB) function was used to roughly smooth the data, with a moving window of 50 points. Third, outliers were removed. All values higher than the smoothed values +10, were replaced with NA. In some cases, further single point outliers were removed. OSU_Met Summary: Raw, 10-minute meteorological observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meteorological stations were located in the approximate center of each forest or open snow course transect. These stations were deployed to collect numerous meteorological variables, of which snow depth and wind speed are included here. These data are raw datalogger output and have not been quality controlled in any way. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN and 7999 flags for missing data to NA, and added site attributes such as site name and cover. OSU_Location Summary: Location Metadata for manual snow course observations and meteorological sensors. These data are compiled from GPS data for which the horizontal accuracy is unknown, and from processed hemispherical photographs. They have not been quality controlled in any way.

This dataset contains occurrence data of flora and fauna species. From the Netherlands on a 5 x 5 km scale, data from other countries are exact. Observations from Belgium are excluded and can be accessed on GBIF through Natuurpunt and Natagora. It summarizes the observations recorded by >175.000 volunteers.

Hatikka.fi observation database. Data quality: Content is not systematically verified. Users are mostly expert amateurs.

Charged Coupled Device (CCD) cameras from UH were used by groups to observe the outburst of comet Halley using a variety of telescopes and chip sets.

The differences between the observations and the forecast background used for the analysis (the innovations or O-F for short) and those between the observations and the final analysis (O-A) are by-products of any assimilation system and provide information about the quality of the analysis and the impact of the observations. Innovations have been traditionally used to diagnose observation, background and analysis errors at observation locations (Hollingsworth and Lonnberg 1989; Dee and da Silva 1999). At the most simplistic level, innovation variances can be used as an upper bound on background errors, which are, in turn, an upper bound on the analysis errors. With more processing (and the assumption of optimality), the O-F and O-A statistics can be used to estimate observation, background and analysis errors (Desroziers et al. 2005). They can also be used to estimate the systematic and random errors in the analysis fields. Unfortunately, such data are usually not readily available with reanalysis products. With MERRA, however, a gridded version of the observations and innovations used in the assimilation process is being made available. The dataset allows the user to conveniently perform investigations related to the observing system and to calculate error estimates. Da Silva (2011) provides an overview and analysis of these datasets for MERRA. The innovations may be thought of as the correction to the background required by a given instrument, while the analysis increment (A-F) is the consolidated correction once all instruments, observation errors, and background errors have been taken into consideration. The extent to which the O-F statistics for the various instruments are similar to the A-F statistics reflects the degree of homogeneity of the observing system as a whole. Using the joint probability density function (PDF) of innovations and analysis increments, da Silva (2011) introduces the concepts of the effective gain (by analogy with the Kalman gain) and the contextual bias. In brief, the effective gain for an observation is a measure of how much the assimilation system has drawn to that type of observation, while the contextual bias is a measure of the degree of agreement between a given observation type and all other observations assimilated. With MERRAs gridded observation and innovation data sets, a wealth of information is available for examination of the quality of the analyses and how the different observations impact the analyses and interact with each other. Such examinations can be conducted regionally or globally and should provide useful information for the next generation of reanalyses.

The national database of deep sea coral observations. Northeast version 1.0. * This database was developed by the NOAA NOS NCCOS CCMA Biogeography office as part of a New York Offshore Spatial Planning project.

The data in this resource consist of biodiversity occurrence records drawn from the National Biodiversity Data Bank's database. The data was clipped using the Albertine Rift boundaries and mapped to Darwin Core terms by the NBDB's manager, Dr Herbert Tushabe.

Data related to 28,019 thermal conductivity observations at locations in Canada, obtained by the Canadian Geothermal Data Compilation. The data table includes general information on the location of the borehole or sample, measurement date, rock name and measurements. Information sources are included in the dataset. The SamplingFeatureURI for a particular sample is the cross-referencing link (foreign key) used to associate the observation with web based information on the feature of interest, including pictures, websites and documents. Data processing to load and aggregate delimited text data from the OFR into a database, and web service deployment by SM Richard and Christy Caudill.

The dataset consists of hourly U.S. surface airways observations (SAO). These observations extend as far back as 1928, from the time when commercial aviation began in the United States and meteorological observing stations were established at many airports (although occasionally, early-period SAO's were taken at U.S. Weather Bureau city offices). For most stations, this dataset extends through June of 1948. The major data variables are as follows: WBAN Station Identification Number, observational type, ceiling and cloud, visibility, present weather data, temperature, wind and pressure. The observations are generally recorded for the 24-hour period midnight to midnight, although many stations did not record 24-hour observations, especially early in the period when commercial aviation was just getting started. Two output keying formats were created to adjust to an observational form change during the period. One format was generally used for years 1928-33, and the other for sets from around 1934 through June of 1948. Each keying format was designed to reflect the data as entered on the observational form for ease of keying by key entry personnel, who were not trained meteorological technicians. The "raw" observations which comprise the DSI-3851 dataset were quality checked, to include data adjustments, and converted to NCDC's Integrated Surface Hourly (ISH) format. The complimentary data to this collection can be found in the Surface Weather Observation 1001 Forms (Keyed) collection.

Data from the content analysis of the observation grids, in the article "Empowerment through Participatory Game Creation: A Case Study with Adults with Intellectual Disability".

Context

This is the eBird Observation Dataset for Brazil, which contains a lot of Bird observation historical data.

What is eBird? Here's what they say: "eBird is a collective enterprise that takes a novel approach to citizen science by developing cooperative partnerships among experts in a wide range of fields: population ecologists, conservation biologists, quantitative ecologists, statisticians, computer scientists, GIS and informatics specialists, application developers, and data administrators. Managed by the Cornell Lab of Ornithology eBird’s goal is to increase data quantity through participant recruitment and engagement globally, but also to quantify and control for data quality issues such as observer variability, imperfect detection of species, and both spatial and temporal bias in data collection. eBird data are openly available and used by a broad spectrum of students, teachers, scientists, NGOs, government agencies, land managers, and policy makers. The result is that eBird has become a major source of biodiversity data, increasing our knowledge of the dynamics of species distributions, and having a direct impact on the conservation of birds and their habitats."

Content

Observed species, number of observations, eBird author ID, location name, location coordinates, timestamp and basis of observation.

Acknowledgements

eBird community and team. GBIF.org for the data availability

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

RBRduo pressure and temperature sensors mounted on aluminum frames, were moored in shallow (4-9 m) water depths along the West side of Whidbey Island, Washington, to measure wave heights and periods. Continuous pressure fluctuations were transformed into surface-wave observations of wave heights, periods, and frequency spectra at 30-minute intervals.

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

We present a comprehensive database of near-shore wind observations that were carried out during the experimental campaign of the RUNE project. RUNE aims at reducing the uncertainty of the near-shore wind resource estimates from model outputs by using lidar, ocean, and satellite observations. Here, we concentrate on describing the lidar measurements. The campaign was conducted from November 2015 to February 2016 on the west coast of Denmark and comprises measurements from eight lidars, an ocean buoy and three types of satellites. The wind speed was estimated based on measurements from a scanning lidar performing PPIs, two scanning lidars performing dual synchronized scans, and five vertical profiling lidars, of which one was operating offshore on a floating platform. The availability of measurements is highest for the profiling lidars, followed by the lidar performing PPIs, those performing the dual setup, and the lidar buoy. Analysis of the lidar measurements reveals good agreement between the estimated 10-min wind speeds, although the instruments used different scanning strategies and measured different volumes in the atmosphere. The campaign is characterized by strong westerlies with occasional storms.

The DAO archives are operated by the Canadian Astronomy Data Centre (CADC). These consist of two separate collections: the DAO Science Archive and the DAO Spectroscopic Plate Archive. The DAO Science Archive consists of modern electronic data obtained with the DAO's 1.8-m Plaskett telescope as well as the 1.2-m telescope and McKellar spectrograph. This collection is updated on a daily basis with new data and, as time permits, archival CCD data are also being added. The DAO spectroscopic plate collection in its entirety consists of over 16,800 high-dispersion spectrograms exposed at the coude focus of the 1.2-m DAO telescope and McKellar spectrograph between 1962 and 2000, and more than 93,000 secured at the Cassegrain focus of the DAO 1.8-m telescope and spectrograph between 1918 and 1984. The very great majority of those plates is now in the NRC-Herzberg plate archive. Since a programme to digitize them with the modified in-house PDS has only recently commenced (and with limited resources) there is currently a rather modest number of digital files available for download. However, if you have questions about the availability of plates of a target of particular interest you can we encourage you to contact Elizabeth Griffin or David Bohlender at NRC-Herzberg (elizabeth.griffin@nrc-cnrc.gc.ca), david.bohlender@nrc-cnrc.gc.ca) so that we can search the collection for plates that may be of interest to your research. Both archives allow searches by important criteria such as object name, date, and wavelength and presents the results of the search in a tabular format. The CADC's Data Retrieval facility allows archive users to download archival data directly to their own computers. Proprietary data can also be retrieved, but only by the Principal Investigator (PI) of the science program in question and any colleagues the PI has granted access to that program's data. These users must also register with the CADC in order to enable authorization tests to be made before such proprietary data is accessed. The proprietary period for DAO pixel data is 12 months from the time of the observation. Metadata associated with the pixel data (i.e. the FITS header) is public immediately. Because of uncertainties in the absolute pointing accuracy of the DAO telescopes, it is recommended that a relatively large search radius (e.g. 5') be used in any DAO archive searches for specific targets.

This data set contains the DYNAMO areal averages of a number of surface and upper air parameters over the DYNAMO region at three hourly intervals. Areal averages are included for two regions, the DYNAMO Northern Sounding Array and the Southern Sounding Array. For each region there are two sets of averages included, one using just observations (DYNAMO radiosondes, satellite wind, TRMM precipitation, and COSMIC profiles) and a second analyses that also included the use of ECMWF operational analyses in data sparse regions. The data are in NetCDF format.

The dataset contains noteworthy species observations of the nature surveys made by Faunatica Oy. The dataset is primarily accompanied by observations from those surveys that require the observations to be saved to FinBIF.

This paper's goal is to improve the member census of the NGC 2264 star-forming region and study the origin of X-ray activity in young PMS stars. We analyze a deep, 100ks long, Chandra ACIS observation covering a 17'x17' field in NGC 2264. The preferential detection in X-rays of low-mass PMS stars gives strong indications of their membership. We study X-ray activity as a function of stellar and circumstellar characteristics by correlating the X-ray luminosities, temperatures, and absorptions with optical and near-infrared data from the literature. Cone search capability for table J/A+A/455/903/sources (Catalog of X-ray ACIS detections and optical/NIR properties) Cone search capability for table J/A+A/455/903/table3 (Master catalog of objects in the ACIS FOV)

This is the archive of CAM6 simulation output used in the paper Archived Data for Simulating Observations of Southern Ocean Clouds and Implications for Climate which appeared in AGU J. ... Advances in Modeling Earth Systems, JGR Atmospheres The CAM6 output data is in CAM history format and includes three types of files: monthly, daily and along aircraft flight tracks.

AUSTIN POLICE DEPARTMENT DATA DISCLAIMER 1. The data provided are for informational use only and may differ from official APD crime data. 2. APD’s crime database is continuously updated, so reports run at different times may produce different results. Care should be taken when comparing against other reports as different data collection methods and different data sources may have been used. 3. The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided.