Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,199
datasets available to search
ShareScore release 0.7.1
Dataset results
3,199 results for “indexing”
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
Index der Flurnamen Tirols
<p>Orts- und Flurnamen spielen eine zentrale Rolle bei der räumlichen Orientierung. Je nach Bedeutung bieten sie auch noch eine Reihe weiterer Informationen zur Landschaft, deren Nutzung sowie der Kultur der einheimischen Bevölkerung. Allerdings geraten besonders Flurnamen aufgrund der veränderten Landnutzung in den letzten Jahrzehnten zunehmend in Vergessenheit.</p> <p><br> Unter anderem im Hinblick auf diese Entwicklung arbeiteten Forscher*innen der Universität Innsbruck in den Jahren 2007 bis 2017 gemeinsam mit hunderten von Ortschronist*innen und lokalen Wissensträger*innen an der Dokumentation der heute noch bekannten Tiroler Flurnamen. Ungefähr 120.000 Flurnamen wurden in den 279 Gemeinden Tirols erhoben und in einem geographischen Informationssystem verortet. Nicht zuletzt aufgrund dieser Dokumentation wurden die Flurnamen im Jahr 2018 in das nationale UNESCO Verzeichnis des immateriellen Kulturerbes Österreichs aufgenommen.</p> <p>Mit dem hier gespeicherten Flurnamenindex können alle im Projekt "Flurnamenerhebung im Bundesland Tirol" gesammelten Namen durchsucht und nach Gemeinden geordnet werden. Über die Identifikationsnummer im Feld "oid_copy" können die Namen an die georeferenzierten Daten der Flurnamenkarte (https://www.uibk.ac.at/projects/flurnamen-tirol/flurnamenkarten/index.html.de) angebunden werden.</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
Flash Flood Severity Index (Flashiness) dataset for the United States
<p>(Saharia et al., 2017)</p> <p>Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.</p>
Forest condition anomaly index values covering Germany for 2016-2023
<p><strong>General description:</strong><br>In <a href="https://doi.org/10.1016/j.rse.2024.114323" target="_blank" rel="noopener">Lange et. al (2024)</a> we utilised <em>Sentinel-2</em> tree species-specific reflectance time series for extracting forest condition across Germany from 2016 to 2022. These time series' seasonal evolution - computed separately for seven natural regions - serves as reference when calculating a similarity metric – further called <em>forest condition anomaly index</em> (FCA). The FCA is computed between each single reflectance observation and the respective date within the reference time series, also considering the natural temporal deviations caused by phenology. FCA temporal aggregation allowed generating spatially comprehensive forest condition anomaly maps. FCA patterns in space and time are in line with dominant drivers like fires, storms and insect infestations and in agreement with state-of-the-art forest disturbance products using a threshold of FCA = −0.15 for forest loss. More information can be found in the <a href="https://doi.org/10.1016/j.rse.2024.114323" target="_blank" rel="noopener">related publication</a> and in the <a title="UFZ Forest condition monitor" href="https://web.app.ufz.de/forestconditionmonitor" target="_blank" rel="noopener">UFZ Forest condition monitor web-application</a>.</p> <p><br><strong>Data description:<br></strong>Data is provided in GeoTiff format (projection <a href="https://epsg.io/32632" target="_blank" rel="noopener">EPSG:32632</a>). Forest condition anomaly maps are available in a spatial resolution of 20 <em>m</em> for the years 2016 to 2023 as monthly (May to October), seasonal (spring, summer and fall) and yearly maps. Values are scaled by 10 000 to reduce the file size. Final FCA values are obtained by dividing the raw values by 10 000 and range from -1 to 1. A negative value generally indicates a poorer forest condition, for example, due to negative changes in chlorophyll or water content or due to crown defoliation. Through validation using forest surveys, data from the <em>Copernicus Emergency Management System</em> and other current maps of forest cover loss, it can be relatively accurate determined that a value below -0.15 indicates a heavily damaged or dead forest stand. Stronger damage (such as significant needle/leaf loss or tree mortality) is generally captured more precise than light damage (such as slight needle/leaf loss). Moderate forest condition values correspondingly show no anomaly and represent the expected normal condition for the respective tree species at the given time within the year. Positive forest condition values indicate a positive deviation from the expected state, which might stem from from positive chlorophyll or water content changes or from denser foliage or needle cover.</p> <p> </p> <p><strong>File descriptions</strong>: <br>Data is provided in zip archives containing maps in GeoTiff format (projection <a href="https://epsg.io/32632" target="_blank" rel="noopener">EPSG:32632</a>). 4 zip files are provided:</p> <ul> <li><em>FCA_v0007-0005_Germany_2016-2023_yearly_R20m.zip</em> contains 8 yearly FCA maps </li> <li><em>FCA_v0007-0005_Germany_2016-2023_seasonal_R20m.zip </em>contains 24 seasonal FCA maps (spring, summer and fall for 2016 to 2023)</li> <li><em>FCA_v0007-0005_Germany_2016-2019_monthly_R20m.zip</em> contains 24 monthly maps (May to October for 2016 to 2019)</li> <li><em>FCA_v0007-0005_Germany_2020-2023_monthly_R20m.zip</em> contains 24 monthly maps (May to October for 2020 to 2023)</li> </ul> <p> </p> <p><strong>Please note:</strong><br>Forest pixels were selected according to the tree species map from <a href="https://doi.org/10.1016/j.rse.2024.114069" target="_blank" rel="noopener">Blickensdörfer et al. (2024)</a>. </p>
IT-VaLex: Index Thomisticus Valency Lexicon
<p>IT-VaLex: <em>Index Thomisticus</em> Valency Lexicon. Website: <a href="https://itreebank.marginalia.it/itvalex/">https://itreebank.marginalia.it/itvalex/</a></p>
Fire Weather Index - ERA-Interim
<p>The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Relative density variations of common vole population based on index transect, Septfontaines - Le Souillot, France (1990-2000)
<p>Transects were walked from village to village along a transect line. Common vole (<em>Microtus arvalis</em>) activity indices were recorded in every ten pace interval from October 1990 to April 2000. In 2014, the geographical coordinates of each interval has been computed by spatial interpolation based on georeferenced maps. Therefore, users must be aware that individual locations of intervals are unprecise, but not the general bearing of the transect in the landscape and interval succession. See articles published for reference and more details.</p> <p>During the same time span, small mammmals (including common voles) were sampled using live-trapping, see <a href="https://doi.org/10.5281/zenodo.6997316">10.5281/zenodo.6997316</a></p> <p><strong>FILE DESCRIPTION:</strong></p> <p><a href="https://zenodo.org/record/7544358/files/db.txt?download=1">db.txt </a>index transect file</p> <ul> <li>name: transect name</li> <li>date: on eight digits, '19921014' reads 14/10/1992</li> <li>ID: interval ID = number (within a given transect at a given date)</li> <li>Habitat: (indicative) the habitat category crossed. Just mentioned when passing from one category to the other; the following intervals are assumed to belong to this habitat</li> <li>ma1: number of <em>Microtus</em> holes; A, 1-5 holes; B, 6-10 holes; C > 10 holes</li> <li>ma2: answered only if A, B, or C are defined in ma1; NA, not answered (ma1 not defined), 0, zero faeces, 1 some faeces or fresh indices (runways with grass freshly cut, etc.); 2 many faeces in heaps</li> <li>long: longitude (WGS84)</li> <li>lat: latitude (WGS84)</li> </ul> <p><a href="https://zenodo.org/record/7544358/files/StudyAreaBoundingBox.kml?download=1">StudyAreaBoundingBox.kml</a> Bounding box of the study area.</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Big Bee indexed biotic interactions and review summary
<p><strong>Extending Anthophila research through image and trait digitization (Big-Bee) indexed biotic interactions and review summary.</strong></p> <p>Declining populations of bees impact plant-pollinator interactions in both natural and agricultural systems. While bees and other insects pollinate most wild plants and are critical to sustaining a large proportion of global food production, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited because we lack sufficient data on the distribution of bee species, and on the behavioral and anatomical traits that may make them either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, wild bees have been collected by researchers and deposited in natural history collections for over 100 years, retaining a wealth of associated attributes that can be extracted from specimen images. This project will digitally capture data and images from these historic specimens, develop tools to measure bee traits from these images and generate a comprehensive bee trait and image dataset to measure changes through time. This will increase our understanding of specific traits that put bee species at risk of decline - a critical need for both sustaining our agricultural economy and the conservation of our natural resources. In addition, the large image datasets created by this project can be used for new artificial intelligence identification tools that will help improve our future pollinator observation and monitoring efforts.</p> <p>The Big-Bee project began in 2021 and is funded by the National Science Foundation to mobilize data about worldwide bee species to data aggregators (e.g., iDigBio, GBIF). The Big-Bee Thematic Collection Network (Big-Bee) will create over one million high-resolution 2D and 3D images of bee specimens, representing over 5,000 worldwide bee species, including all of the major pollinating species of the United States. The Big-Bee network includes 13 institutions and partnerships with US government agencies. Novel mechanisms for sharing image datasets will be developed and datasets of bee traits will be available through an open data portal, the Bee Library, for research and education. The Big-Bee project will engage the general public in research through community science via crowdsourcing trait measurements and data transcription from images. In addition, training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. All data resulting from this award will be shared with and publicly available through the national digitized biocollections resource, iDigBio.org.</p> <p>This is the first archive of Big-Bee data indexed by Global Biotic Interactions (GloBI). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open-source software. This version of the Big Bee dataset includes interactions that are not just bees. Also in this version, the datasets included in this publication are specifically those institutions in the Big Bee project network and do not represent all bee interaction data found at Global Biotic Interactions.</p> <p><strong>Bee Library Information - Statistics about Big Bee data providers</strong></p> <p>The specimens indexed by GloBI are also found in the <a href="https://library.big-bee.net/portal/">Bee Library</a>. To date, the number of specimens and images in the library are listed below. The Bee Library taxonomic backbone is not yet complete, so information regarding the number of species is not yet available. Further summary statistics are available in the Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf file.</p> <p><strong>From Bee Library (partner indexed records)</strong><br> 1,234,107 occurrence records<br> 993,692 (81%) georeferenced<br> 351,592 (28%) occurrences imaged<br> 986,323 (80%) identified to species<br> 9 families<br> 526 genera<br> 10,700 species<br> 11,386 total taxa (including subsp. and var.)</p> <p><strong>Statistics Per Collection</strong></p> <table> <tbody> <tr> <td>Collection</td> <td>Occurrences</td> <td>Georeferenced</td> <td>Imaged</td> <td>Interactions Indexed in GloBI (all)</td> <td>Interactions Indexed in GloBI (bees)</td> </tr> <tr> <td>ASU Hasbrouck Insect Collection - Bee<br> Records</td> <td>13223</td> <td>13221</td> <td>2352</td> <td>21300</td> <td>3834</td> </tr> <tr> <td>Bee Biology and Systematics Laboratory,<br> USDA-ARS Pollinating Insect-Biology,<br> Management, Systematics Research</td> <td>561820</td> <td>547461</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <td>California Academy of Sciences</td> <td>884</td> <td>300</td> <td>3</td> <td>16984</td> <td>117</td> </tr> <tr> <td>California Academy of Sciences - Type<br> Collection</td> <td>1838</td> <td>59</td> <td>83</td> <td>0</td> <td>0</td> </tr> <tr> <td>Essig Museum of Entomology, University<br> of California Berkeley</td> <td>58551</td> <td>55028</td> <td>0</td> <td> </td> <td>0</td> </tr> <tr> <td>Florida State Collection of Arthropods</td> <td>17134</td> <td>12349</td> <td>7816</td> <td>559</td> <td> </td> </tr> <tr> <td>Museum of Comparative Zoology, Harvard<br> University</td> <td>22020</td> <td>21099</td> <td>11595</td> <td>6777</td> <td>1535</td> </tr> <tr> <td>Natural History Museum of Los Angeles<br> County</td> <td>24685</td> <td>7421</td> <td>3480</td> <td>0</td> <td>0</td> </tr> <tr> <td>San Diego Natural History Museum<br> Entomology Department</td> <td>4065</td> <td>1690</td> <td>1982</td> <td>8688</td> <td>90</td> </tr> <tr> <td>University of California Santa Barbara<br> Invertebrate Zoology Collection</td> <td>8674</td> <td>8410</td> <td>2751</td> <td>1940</td> <td>660</td> </tr> <tr> <td>University of Colorado Museum of Natural<br> History, Entomology Collection</td> <td>18043</td> <td>18043</td> <td>0</td> <td>9589</td> <td>4723</td> </tr> <tr> <td>University of Kansas Natural History<br> Museum Entomology Division</td> <td>464927</td> <td>275200</td> <td>304415</td> <td>119963</td> <td>112677</td> </tr> <tr> <td>University of Michigan Museum of Zoology<br> Division of Insects</td> <td>17764</td> <td>15305</td> <td>15269</td> <td>53755</td> <td>4134</td> </tr> <tr> <td>University of New Hampshire, Donald S.<br> Chandler Entomological Collection</td> <td>17685</td> <td>17393</td> <td>0</td> <td>3137</td> <td>3137</td> </tr> <tr> <td>USGS Native Bee Inventory and Monitoring<br> Lab</td> <td>101</td> <td>101</td> <td>0</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <p><strong>GloBI Data Review Report - Datasets in Review from Global Biotic Interactions</strong></p> <p>Datasets under review:<br> - UUniversity of Michigan Museum of Zoology, Division of Insects accessed via https://github.com/globalbioticinteractions/ummz-ummzi/archive/d9282e51f29f3157af2e5869a09ea8a111ddea34.zip on 2023-07-24T22:06:08.671Z<br> - Arizona State University Hasbrouck Insect Collection accessed via https://github.com/globalbioticinteractions/asu-asuhic/archive/4ed77cb9ca8e526269d4678692e2844c950022f8.zip on 2023-07-24T22:07:09.630Z<br> - California Academy of Sciences Entomology and Entomology Type Collection accessed via https://github.com/globalbioticinteractions/cas-ent/archive/47d385b73a63aa379cd5e6d3615005ba78b0ffc1.zip on 2023-07-24T22:08:13.753Z<br> - University of California Berkeley, Essig Museum of Entomology accessed via https://github.com/globalbioticinteractions/emec/archive/93b17a3db566baa001ce9190e6fbdb60fa99dda4.zip on 2023-07-24T22:08:24.495Z<br> - Florida State Collection of Arthropods accessed via https://github.com/globalbioticinteractions/fsca/archive/2cdcf9475b7e0ef2a728a96535608bc0ce2ac5ca.zip on 2023-07-24T22:08:49.972Z<br> - University of Kansas Natural History Museum accessed via https://github.com/globalbioticinteractions/ku-semc/archive/a9c7cb81050eef68b4428667206a219da458f517.zip on 2023-07-24T22:09:17.016Z<br> - Natural History Museum of Los Angeles County accessed via https://github.com/globalbioticinteractions/lacm-lacmec/archive/dafbf532c53fbadba126c81186c26d52677aa781.zip on 2023-07-24T22:11:11.442Z<br> - Harvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University. accessed via https://github.com/globalbioticinteractions/mcz/archive/b33635a9fc75fd7931ad968cbc11180e6467bfd7.zip on 2023-07-24T22:21:32.961Z<br> - San Diego Natural History Museum accessed via https://github.com/globalbioticinteractions/sdnhm-sdmc/archive/7238d8b804f543250eb487b43144e1125fb3688a.zip on 2023-07-24T22:26:25.503Z<br> - University of Colorado Museum of Natural History Entomology Collection accessed via https://github.com/globalbioticinteractions/ucm-ucmc/archive/60530dcc82d33c9675a4026ad60dc40bea8f2a91.zip on 2023-07-24T22:26:50.178Z<br> - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/66a4e39589d1dfa299d07985546c4be522ff60d8.zip on 2023-07-24T22:27:13.801Z<br> - University of New Hampshire Donald S. Chandler Entomological Collection accessed via https://github.com/globalbioticinteractions/unhc-unhc/archive/d7668a6bb4545dc4da0645ecc383169ba547b0f5.zip on 2023-07-24T22:27:28.670Z</p> <p>Generated on:<br> 2023-07-24</p> <p>by:<br> GloBI's Elton 0.12.6 <br> (see https://github.com/globalbioticinteractions/elton).</p> <p>Note that all files ending with .tsv are files formatted <br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv: <br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar: <br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p>indexed_interactions_bees.tsv:<br> All indexed bee interactions <br> </p> <p>datasets.zip:<br> All datasets reviewed for this publication</p> <p> Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf:<br> Summary statistics from the Bee Library and GloBI about data partners</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/Big-Bee-Network/issues-observations-and-questions/discussions or contact the authors by email.</p> <p><strong>Funding:</strong><br> The creation of this archive was made possible by the National Science Foundation award Collaborative Research: Digitization TCN: Extending Anthophila research through image and trait digitization (Big-Bee). Award numbers: <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102006">DBI:2102006</a>, DBI:2101929, DBI:2101908, DBI:2101876, DBI:2101875, DBI:2101851, DBI:2101345, DBI:2101913, DBI:2101891 and DBI:2101850.</p> <p>References:<br> Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Jorrit Poelen, Tobias Kuhn, & Katrin Leinweber. (2022). globalbioticinteractions/elton: 0.12.5 (0.12.5). Zenodo. https://doi.org/10.5281/zenodo.7267926</p>
The Updated Investment Facilitation Index
<p>The Investment Facilitation Index (IFI) provides information on the current adoption of investment facilitation measures at country level for 142 World Trade Organisation (WTO) Members. It was developed by the German Institute of Development and Sustainability (IDOS), previously known as the Deutsches Institut für Entwicklungspolitik / German Development Institute (DIE), in cooperation with the WTO. The IFI is a composite index measuring the adoption of investment facilitation measures in 2021 and applying a multiple binary scoring scheme. Departing from an earlier version of the index (<a href="https://doi.org/10.23661/dp23.2021">Berger et al., 2021</a>), it has been conceptually revised and extended regarding its country coverage. It now consists of 101 measures composing six regulatory dimensions and corresponds closely to the main policy areas and developments within current policy debates, including the newly negotiated <a href="https://www.wto.org/english/news_e/news23_e/infac_06jul23_e.htm">Investment Facilitation for Development (IFD) Agreement</a> among the WTO Members.</p> <p>The data set provides the foundation for analysing specific facilitation hurdles in investment frameworks of a large number of economies. The fine grained data of the IFI can be used for investigating economic benefits and challenges of investment facilitation reforms, support the assessment of implementation gaps, as well as prioritisation of technical assistance and capacity development. It can also be used by investors seeking information on a country’s investment regime.</p> <p>For a detailed description of the methodology and coding of the IFI, please have a look at the uploaded data documentation, contained in the file <strong>ifi_documentation.pdf</strong>. It provides information on the conceptual composition of the index, its evolution from the first version, as well as the coding, data generation and validation processes. In the annex, it also features a detailed overview of each measure contained in the index.</p> <p>The file <strong>ifi_codebook.csv</strong> contains the codebook for the 101 investment facilitation measures included in the IFI. The file features six variables (columns):</p> <ul> <li>Measure: The code of a measure under observation;</li> <li>Area: Specification of the policy area a given measure belongs to;</li> <li>Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;</li> <li>Weight: Specification of the individual weight of a measure, the product of the allocated score (0, 1 or 2) and this weight denotes the contribution to the total score of a given measure;</li> <li>Unit: The measurement unit for the answer of a given measure, it can take values "Score", meaning that the answer is directly measured by score from the multiple binary scoring scheme, or specify another measurement unit, e.g. number of documents, days, US Dollars, etc.;</li> <li>Coding_0: Specifies the answer coding which allocates a score of 0 to this measure;</li> <li>Coding_1: Specifies the answer coding which allocates a score of 1 to this measure;</li> <li>Coding_2: Specifies the answer coding which allocates a score of 2 to this measure.</li> </ul> <p>The file <strong>ifi_table.csv</strong> or <strong>ifi_table.xlsx </strong>(please choose your preferred file format) contains all 14484 data points resulting from the 101 measures coded for 142 economies. Moreover, it also contains the total score for each country calculated by applying the expert weighting scheme. The file contains the following variables (columns):</p> <ul> <li>CountryCode: <a href="https://unstats.un.org/unsd/methodology/m49/">ISO-alpha3</a> code of a country for which a given measure is coded;</li> <li>Country: Name of a country for which a given measure is coded;</li> <li>Measure: Code of a measure that is coded in a given row;</li> <li>Area: Specification of the policy area a given measure belongs to;</li> <li>Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;</li> <li>Answer: The answer coded for a given measure and country;</li> <li>Score: The allocated score based on the answer, according to the definition of the measure (see codebook);</li> <li>Unit: The measurement unit for the answer of a given measure;</li> <li>Coding: The answer option coded for a given measure and country (corresponds to either Coding_0, Coding_1 or Coding_2 in the codebook);</li> <li>Source: Source statement for the provided answer.</li> </ul> <p>For further inquiries please contact the authors.</p>
Salt marsh decomposition rates using Tea Bag Index in two Southeastern MA marshes in the towns of Fairhaven and Dartmouth from 2020-2022.
Natural disturbances, sea level rise, and historic human impacts to salt marshes have increased impounded water on marsh surfaces, resulting in vegetation loss and the associated loss of important ecosystem services such as carbon storage. Runnels are a climate adaptation technique designed to restore salt marsh habitat by reestablishing a tidal connection between impounded water and a nearby drainage feature. However, panne formation and runnel restoration can alter carbon decomposition. We measured decomposition using the Tea Bag Index at two marshes, one in Fairhaven and the other in Dartmouth, MA. We repeated measurements for three years: 1 year prior to runnel creation, and two years post. We measured decomposition at three spatial zones, the center of the panne, landward of the panne, and seaward of the panne.
Leaf area index (LAI) by plant functional group in moist acidic tussock tundra, at the 2007 Anaktuvuk River fire scar measured in 2017
This file contains leaf area index (LAI) based on biomass measurements from an aboveground pluck in the southern portion of the Anaktuvuk River fire scar, and a nearby unburned site in late July 2017. Vegetation was sampled randomly at 10-m intervals along two 100 meter transects at both the burned and unburned sites. Vegetation was sampled within a 10X40 cm quadrat to the mineral layer, and plant material was sorted into new and old aboveground leaf and woody biomass by species. All samples were dried and weighed, and subsampled leaf material was scanned to determine specific leaf area (centimeterSquaredPerGram biomass) per species, which was then used to transform leaf biomass (gramPerMeterSquared) into the leaf area index for each site.
Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.