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.

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.

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

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.

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".

A heterogeneous dataset of human measurement data and human-generated text. This dataset was generated by TechSolve Inc. (techsolve.org) as a collaborative effort with NIST. Respondents were asked to observe and evaluate a machining process in which a rotary bit (the "tool") removed layers of a workpiece until the tool was worn to exhaustion. One trial and 19 official experiments were completed, one for each of 20 tools. Respondents were tasked with completing a survey in which they recorded their measurements of tool flank wear along all four tool chamfers, recorded their description of the cut in natural language, and rated the process and tool condition on a Likert scale, along with other measurements.

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?

Student Performance Data

This dataset provides insights into various factors influencing the academic performance of students. It is curated for use in educational research, data analytics projects, and predictive modeling. The data reflects a combination of personal, familial, and academic-related variables gathered through observation or survey.

The dataset includes a diverse range of students and captures key characteristics such as study habits, family background, school attendance, and overall performance. It is well-suited for exploring correlations, visualizing trends, and training machine learning models related to academic outcomes.

Highlights:

Clean, structured format suitable for immediate use Designed for beginner to intermediate-level data analysis Valuable for classification, regression, and data storytelling projects

File Format:

Type: CSV (Comma-Separated Values) Encoding: UTF-8 Structure: Each row represents a student record

Applications

Student performance prediction Educational policy planning Identification of performance gaps and influencing factors Exploratory data analysis and visualization

Salinity data was compiled from data provided by different agencies around the Gulf of Mexico, research projects and cruises.

Data source: National Water Quality Monitoring Council (NWQMC), Environmental Protection Agency (EPA), United States Geological Survey (USGS), National Estuarine Research System (NERRS), Texas Commission on Environmental Quality (TCEQ), Florida Keys National Marine Sanctuary (FKNMS), National Park Water Services (NPWS), Louisiana Department of Environmental Quality (LDEQ), Louisiana Universities Marine Consortium (LUMCON), Mississippi Department of Environmental Quality (MDEQ), Alabama Department of Environmental Management (ADEM), Florida Department of Environmental Protection (FDEP) and Texas A&M University (TAMU).

This data provides an interpolated raster surface of dissolved oxygen values across a region covered by an August 23, 2016 AUV survey. The raster was generated by using a natural neighbors interpolator within a GIS on the empirical data set. This interpolator was chosen due to the non normal distribution observed among the data, and its ability to produce smoother approximations than alternative interpolation methods. During the August 23 survey, 13,910 data points were collected. A subset of 3477 (25%) random points were removed prior to interpolation. The interpolated raster values were compared with the subset to diagnose the accuracy of interplotion. 3076 out of 3477 (88.45) of the points were within 97.5% of the raster value.

Dataset Card for "Observation or evaluation"

  Dataset Summary

This dataset contains statements classified into observations and evaluations categories, based on the principles of Nonviolent Communication (NVC) teached by Marshall Rosenberg. It includes a synthetic dataset generated and augmented through various language models to classify statements reflecting either pure observations (noticing) or evaluations (judgments), aimed at understanding and practicing effective… See the full description on the dataset page: https://huggingface.co/datasets/thomasgauthier/observation_or_evaluation.

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.

The ocean dynamic datasets of seafloor observation network experiment system at the South China Sea was completed in September 2016. This system provided energy supply and communication transmission channel through optical fiber composite power cable for the deep ocean observation platform, enabling multi-parameter, real-time and continuous ocean observation. The Subsea Dynamic Platform with CTD and ADCP was deployed in June 2017, and the collection of observation data was started from July 2017, including the collection of temperature, conductivity, water pressure from CTD and velocity from ADCP. Based on the raw observation data collected by ADCP and CTD sensors from July 2017 to December 2018, the data processing and quality control algorithm is adopted to remove outliers, add missing values, format the data and finally produce the dataset. The dataset consists of 4 data files in total: Ocean dynamic datasets of South China Sea 2017 - ADCP.CSV, totaling 1.12 MB, Ocean dynamic datasets of South China Sea 2018 - ADCP.CSV, totaling 2.24 MB, Ocean dynamic datasets of South China Sea 2017 – CTD.CSV, totaling 35.6 MB, Ocean dynamic datasets of South China Sea 2018 - CTD.CSV, totaling 73 MB.

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.

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.

This data set contains continuous water level, specific conductance, and temperature measured in KAHO 1 (State Well No. 4061-001), KAHO 2 (State Well No. 4161-002), KAHO 3 (State Well No. 4161-001), and Aimakapa Fishpond (2-inch PVC monitoring pipe) by the National Park Service (NPS) and the U.S. Geological Survey (USGS) Pacific Island Water Science Center during the period 2007 to present. The wells are locally known as the Visitor Center Well, the Upper Kaloko Road Well, and the Lower Kaloko Road Well, respectively. These data were processed for quality assurance/quality control by the USGS. This data set also includes station documents (Station Analysis, Station Description, and Manuscript) prepared by the USGS for the periods when the NPS conducted data collection. The monitoring data in this data set can be viewed online at the external links provided in this reference.

This part of USGS Data Series 935 (Cochrane, 2014) presents observations from underwater video collected in the Offshore of Seattle, California, map area, a part of the Southern Salish Sea Habitat Map Series. To validate the interpretations of multibeam sonar data and turn it into geologically and biologically useful information, the U.S. Geological Survey (USGS) towed a camera sled over specific locations throughout the Seattle map area to collect video and photographic data that would “ground truth” the seafloor. The ground-truth survey conducted in the Offshore of Seattle map area occurred in 2011 on the R/V Karluk (USGS field activity K0111PS) and on the Washington State Department of Fish and Game R/V Molluscan (USGS field activity M0111PS). The underwater camera sled was towed 1 to 2 m above the seafloor at speeds of between 1 and 2 nautical miles/hour. The surveys for this map area include approximately 6 hours (9.1 trackline km) of video.

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 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.

This table records high-level information for the observations obtained with Hitomi and provides access to the data archive. The Hitomi mission was launched on a JAXA H-IIA into low Earth orbit on February 17, 2016, at 5:45 pm JPS from Tanegashima Space Center. Hitomi was equipped with four different instruments that together cover a wide energy range 0.3-600 keV. The Soft X-ray Spectrometer (SXS), which combined a lightweight Soft X-ray Telescope paired with a X-ray Calorimeter Spectrometer, provided non-dispersive 7-eV resolution in the 0.3-10 keV bandpass with a field of view of about 3 arcminutes. The Soft X-ray Imager (SXI) expanded the field of view with a new generation CCD camera in the energy range of 0.5-12 keV at the focus of the second lightweights Soft X-ray Telescope; the Hard X-ray Imager (HXI, two units) performed sensitive imaging spectroscopy in the 5-80 keV band; the non-imaging Soft Gamma-ray Detector (SGD, two units) extended Hitomi's energy band to 600 keV. On March 27, 2016, JAXA lost contact with the satellite and, on April 28, announced the cessation of the efforts to restore mission operations. At that time Hitomi was in check-out phase and had started the calibration observations. Data were collected from six celestial objects (Perseus, N132D, IGR_J16318-4848, RXJ1856.5-3754, G21.5-0.9, and Crab) as well as black sky for a total of about one month of data. The data from these observations were divided into intervals of one day if the observation of a specific pointing was longer that one day. A sequence number was assigned to each observing day and within data from all instruments are included. The day division was mainly to limit the data size within a sequence number. There are in total 42 sequences, and each record in this database table is dedicated to a single sequence. The early observations do not contain data from all instruments and in cases the object was not always placed at the aim point. This database contains parameters to indicate which instrument was on and if the celestial source was in the field of view. The SXS was the first instrument to turn on and therefore all observations contain SXS data, although the thermal equilibrium was reached after March 4 2016. The second instrument was the SXI followed by the HXIs and, finally, the two SGDs. This database table was generated at the Hitomi Science Data Center processing site (Angelini, L., Terada, Y, et al., 2016, SPIE 9905E, 14) with additions to indicate which instrument was on and if the source was in the FOV. It was ingested into the HEASARC database in June 2017. This is a service provided by NASA HEASARC .