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.
9,113
datasets available to search
ShareScore release 0.9.0
Dataset results
9,113 results for “Sampling”
РИС. 1. ОЗеро Дальнее (А) и место обнаруЖениЯ Beringiana beringiana в оЗере (B, С), песчаный грунт в месте обитаниЯ беЗЗубок (D). Стрелками обоЗначено место сбора моллюсков. FIG. 1. Lake Dalneye (A) with the sampling site (B, C), sandy bottom in the habitat of mollusks (D). The arrows indicate the site of material sampling. in Первые данные о морфологии глохидиев двустворчатых моллюсков Beringiana beringiana (Bivalvia, Unionidae) оЗера Дальнее, Камчатка
РИС. 1. ОЗеро Дальнее (А) и место обнаруЖениЯ Beringiana beringiana в оЗере (B, С), песчаный грунт в месте обитаниЯ беЗЗубок (D). Стрелками обоЗначено место сбора моллюсков. FIG. 1. Lake Dalneye (A) with the sampling site (B, C), sandy bottom in the habitat of mollusks (D). The arrows indicate the site of material sampling.
Chemical analysis dataset for contaminants of emerging concern and bioanalytical data for samples from a low mountain stream in Central Germany
<p>In 2022, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. In addition, the extracts were analysed in a bioanalytical test battery using effect-based tools. The battery included assays for cytotoxicity (neutral red retention assay), oxidative stress (Nrf2-CALUX®), endocrine disruption (ER-, AR-, anti-ER-, anti-AR-, GR- and PR-CALUX®) and the fish embryotoxicity test with zebrafish (<em>Danio rerio</em>). The data obtained are included in the .csv files in this repository.</p>
Рис. 13. Частотно-раЗмерное распределение створок спиЗулы сахалинской (Spisula sachalinensis) иЗ раковинной кучи (все выборки). Fig. 13. Size-frequency distribution of valves of Spisula sachalinensis from the shell-midden (all samples). in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 13. Частотно-раЗмерное распределение створок спиЗулы сахалинской (Spisula sachalinensis) иЗ раковинной кучи (все выборки). Fig. 13. Size-frequency distribution of valves of Spisula sachalinensis from the shell-midden (all samples).
Рис. 9. Частотно-раЗмерное распределение створок устрицы (Crassostrea gigas) иЗ раковинной кучи (все выборки). Fig. 9. Size-frequency distribution of valves of the giant oyster (Crassostrea gigas) from the shell-midden (all samples). in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 9. Частотно-раЗмерное распределение створок устрицы (Crassostrea gigas) иЗ раковинной кучи (все выборки). Fig. 9. Size-frequency distribution of valves of the giant oyster (Crassostrea gigas) from the shell-midden (all samples).
Data set for BA and BQ samples
<p><span>Collection of data for the manuscript entitled</span></p> <p><span>Carbonized apples and quinces stillage for electromagnetic shielding</span></p> <p><span>Data for BA and BQ is a collection of data - file is manuscript, file type .pdf</span></p> <p><span>FTPO_TGA_BA.txt – thermogravimetric data of carbon-based nanomaterial produced from biomass apple, file type .txt</span></p> <p><span>VINCA_Raman_BA.txt – Raman spectra data of BA, file type .txt</span></p> <p><span>FTPO_TGA_BQ.txt - thermogravimetric data of carbon-based nanomaterial produced from biomass quince, file type .txt</span></p> <p><span>UniOldenburg_VNA_BA.txt – Vector network analysis of shielding efficiency BA, file type .txt</span></p> <p><span>UniOldenburg_VNA_BQ.txt – Vector network analysis of shielding efficiency BQ, file type .txt</span></p> <p><span>VINCA_Raman_BQ.txt – Raman spectra data of BQ, file type .txt</span></p> <p> </p>
Gwendolyn Koldofsky Memorial: Remarks by Steven Sample, Larry Livingston, Alan Smith and Marilyn Horne
<p>Previously unpublished, private recording sponsored and organized by Alan Smith of the Gwendolyn Koldofsky Memorial event held in October 1999 at Bovard Auditorium at the University of Southern California. Elvia Puccinelli and Lisa Sylvester discovered the recording while cleaning out Smith's office in June 2024 following his death in October 2023. Published here in part: opening remarks of USC President Steven Sample, as well as spoken tributes by Dean of the Music School, Larry Livingston, Alan Smith and Marilyn Horne, with permission. </p> <p>This material is published in connection with the monograph "Accompanying in America: Contextualizing Collaborative Piano" by Chanda VanderHart et al (2025)</p> <p> </p> <p> </p>
nanoCT data for the samples published in the article doi 10.1002/jemt.24746
<p>nanoCT data for the samples published in the article "Dealing with Missing Angular Sections in NanoCT Reconstructions of Low Contrast Polymeric Samples Employing a Mechanical In Situ Loading Stage" - Rafaela Debastiani, Chantal Miriam Kurpiers, Enrico Domenico Lemma, Ben Breitung, Martin Bastmeyer, Ruth Schwaiger, Peter Gumbsch</p> <p><a href="https://doi.org/10.1002/jemt.24746">https://doi.org/10.1002/jemt.24746</a></p> <p>Samples:<br>Sample A_180°_phase<br>Sample A_140°_pos0°_phase<br>Sample A_140°_pos0°_absorption<br>Sample A_140°_pos45°_phase<br>Sample A_140°_pos45°_absorption<br>Sample A_140°_pos90°_phase<br>Sample A_140°_pos90°_absorption</p> <p>Sample B_140°_pos0°_phase<br>Sample B_140°_pos90°_absorption<br>Sample B_140°_pos90°_phase<br>Sample B_coated_140°_absorption<br>Sample B_coated_140°_phase</p> <p> </p> <p>Phase = Zernike phase contrast</p> <p>Absorption = absorption contrast</p>
Global Pasture Watch - Grassland sampling design derived by Feature Space Coverage Sampling (FSCS) at 1-km spatial resolution
<p>Sampling design used in the production of the <strong>global maps of grassland dynamics 2000–2022 at 30 m spatial resolution</strong> in the scope of the Global Pasture Wath initiative. The sampling desing was based in Feature Space Coverage Sampling and resulted in 10,000 sample tiles (1x1 km) distributed across the World, which were visual interpreted in Very-High Resolution imagery thorugh the QGIS plugin <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS Fast Grid Inspection</a>.</p> <p>FSCS steps include:</p> <ul> <li>Short vegetation mask that includes all pixels mapped as mosaic, shrubland, grassland, and sparse vegetation in at least one year from 1993 to 2021 according to <a href="https://www.esa-landcover-cci.org/">ESA/CCI global land cover</a> (<code>gpw_short.veg.mask_esacci.lc_p_1km_s_19920101_20201231_go_epsg.3857_v1.tif</code>),</li> <li>87 input raster layers (including vegetation indices, terrain, land temperature, climate and water variable),</li> <li>Principal Components Analysis (PCA) using all input layers,</li> <li>Selection of the 10 first components (explaining 75% of variance),</li> <li> K-Means with 10,000 clusters (targeted number of samples - <br><code>gpw_grassland_fscs.kmeans.cluster_c_1km_20000101_20221231_go_epsg.3857_v1.tif</code>)</li> <li>Calculation of euclidean distance (in the principal component space) of all 1-km pixels to the centre of each cluster,</li> <li>Selection of the pixel with the shortest distance for each cluster,</li> <li>Conversion of the selected pixels into sample tiles ()</li> </ul> <p>The file <code>gpw_grassland_fscs_tile.samples_1km_20000101_20221231_go_epsg.3857_v1.gpkg</code> provides the sample tiles and include the follow collumns:</p> <ul> <li><strong>X</strong>: Latitude in Web Mercator projection (EPSG:3857),</li> <li><strong>Y</strong>: Longitude in Web Mercator projection (EPSG:3857),</li> <li><strong>cluster_id</strong>: K-Means output ranging from 0—9999,</li> <li><strong>cluster_distance</strong>: Distance from the selected sample to the centre of the cluster,</li> <li><strong>cluster_size</strong>: Number o 1-km pixels inside the K-Means cluster, estimated using Web Mercator projection (<a href="https://epsg.io/3857">EPSG:3857</a>)</li> <li><strong>cluster_size_equal_area</strong>: Number o 1-km pixels inside the K-Means cluster, estimated using Goode Homolosine Land projection (<a href="https://epsg.io/54052">ESRI:54052</a>)</li> <li><strong>cluster_size_corr</strong>: Correction factor to adjust the area distortion due to Web Mercator projection, estimated by the difference in normalized propotional values of cluster_size and cluster_size_equal_area.</li> <li><strong>rf_n_pred</strong>: Number of pixels predicted by a RF model trained to estimate probability to select the pixel closer to the centre of the KMeans cluster. The RF models were trained individually per each cluster using the 10 first components derived by PCA (<code>gpw_comps_fscs.pca_m_1km_20000101_20221231_go_epsg.3857_v1.tar.gz</code>).</li> <li><strong>rf_samp_prob</strong>: Sampling probability based on RF model (<em>rf_n_pred / cluster_size</em>)</li> <li><strong>rf_samp_wei</strong>: Sampling weight estimated in Web Mercator projection.</li> <li><strong>rf_samp_wei_coor</strong>: Corrected sampling weight estimated in Goode Homolosine Land projection.</li> </ul> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Effectively controlling for sample relatedness in large-scale GWAS: application to 79 EHR-derived longitudinal traits
<p><span>Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPA<sub>GRM</sub>, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPA<sub>GRM</sub> can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPA<sub>GRM(CCT)</sub> to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPA<sub>GRM</sub> maintains well-controlled type I error rates and SPA<sub>GRM(CCT)</sub> can serve as a broadly effective method. Applying SPA<sub>GRM</sub> to 79 longitudinal traits extracted from</span><span> </span><span>UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.</span></p>
Caravan-DE: Caravan extension Germany - German dataset for large-sample hydrology
<p><em>Caravan</em> is an open community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world:<a href="../record/6578598" target="_blank" rel="noopener"> https://zenodo.org/record/6578598</a>. <br>We have employed the published code to derive meteorological forcing data and catchment attributes from global data sources to extend Caravan with data for <strong>1887 catchments</strong> in Germany. The time series data are in <strong>daily resolution and span up to 70 years, from January 1951 to December 2020</strong>.</p> <p>Most of the catchments in Caravan-DE are also part of the CAMELS-DE dataset (<a href="https://doi.org/10.5281/zenodo.13837553" target="_blank" rel="noopener">10.5281/zenodo.13837553</a>, 1582 catchments). As CAMELS-DE relies on meteorological forcing data that is only available within the borders of Germany, catchments going beyond the German national borders had to be discarded. Caravan uses global data products for the meteorological forcing data and catchment attributes, which is why Caravan-DE includes these catchments that are partly located outside of Germany. As catchments in Caravan-DE and CAMELS-DE are identified by the same ID, the datasets can be used together.<br>Please refer to the CAMELS-DE paper (<a href="https://doi.org/10.5194/essd-2024-318">https://doi.org/10.5194/essd-2024-318</a>) for information about discharge data and the catchment geometries used for both Caravan-DE and CAMELS-DE.</p> <p>For the processing of the data the following guide was followed step by step: <a href="https://github.com/kratzert/Caravan/wiki/Extending-Caravan-with-new-basins">https://github.com/kratzert/Caravan/wiki/Extending-Caravan-with-new-basins</a></p> <h3> </h3> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <p> </p> <h3>Changelog</h3> <ul> <li>v1.0.1 <ul> <li>Small changes to be consistent with other Caravan extensions (see <a href="https://github.com/kratzert/Caravan/issues/35" target="_blank" rel="noopener">https://github.com/kratzert/Caravan/issues/35</a>): <ul> <li>camelsde_basin_shapes.shp: removed column gauge_name</li> <li> <div>attributes_other_camelsde.csv: added river name to gauge_name column</div> </li> </ul> </li> </ul> </li> <li>v1.1.0 <ul> <li>Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climate indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). <br>This was conducted to be consistent with <a href="https://doi.org/10.5281/zenodo.14673536" target="_blank" rel="noopener">Caravan version 1.5</a>.</li> </ul> </li> <li>v1.1.1 <ul> <li>By mistake, the new variables for v1.1.0 were missing in the climatic indices, these have now been added.</li> </ul> </li> </ul>
ExpertSegmentation: Sample datasets for segmentation of microscopy with domain-informed targets via custom loss
<p>Sample datasets for submission to NeurIPS 2024 titled ExpertSegmentation: Segmentation for microscopy with domain-informed targets via custom loss. Hand-labels were generated using Ilastik. Files include:</p> <ol> <li><strong>NMC_3D</strong>: 3D, 2-phase, microCT image of a Lithium-ion battery LiNiMnCoO2 electrode</li> <li><strong>NMC_3D_Labels: </strong>Hand labels for NMC_3D volume.</li> <li><strong>NMC_2D</strong>: 2D, 4-phase, SEM image of a Lithium-ion battery LiNiMnCoO2 electrode cross-section</li> <li><strong>NMC_2D_Labels: </strong>Hand labels for NMC_2D image.</li> <li><strong>Graphite_3D</strong>: 3D, 3-phase, microCT image of a Laser-ablated lithium-ion battery graphite electrode</li> <li><strong>Graphite_3D_Labels: </strong>Hand labels for Graphite_3D volume.</li> <li><strong>PEMFC_3D</strong>: 3D, 5-phase, microCT image of a Polymer Electrolyte Membrane Fuel Cell (PEMFC)</li> <li><strong>PEMFC_3D_Labels: </strong>Hand labels for PEMFC_3D volume.</li> </ol> <p>See references for dataset sources.</p>
Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH)
<p>CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland) is a large-sample hydro-meteorological data set for hydrological Switzerland in Central Europe that covers 331 basins within Switzerland and neighboring countries (Austria, France, Germany and Italy). CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes.</p> <p>The data set covers 40 years of data between 1st January 1981 and 31st December 2020 for each catchment: daily time series of stream flow and water levels, of meteorological data such as precipitation and air temperature and of daily snow water equivalent data. Additionally, CAMELS-CH encompasses annual time series of land cover change and glacier evolution per catchment. The static catchment attributes comprise the following categories: location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers.</p> <p>The corresponding manuscript is published at the journal "Earth System Science Data" (ESSD) and available <a href="https://essd.copernicus.org/articles/15/5755/2023/">here</a>. The code used to generate the dataset is available on <a href="https://github.com/camels-ch">Github</a>.</p> <p>The data description file below contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources. Further, this repository contains the "Caravan extension CH" for the "Caravan - A global community dataset for large-sample hydrology" <a href="../records/7944025">Caravan dataset</a> (see the <a href="https://github.com/kratzert/Caravan/discussions/10">list of extensions</a>). This extension has the same format like other Caravan parts and is based on the same data sources. Note that some features like the annual glacier time series, etc. are therefore only available in the original CAMELS-CH dataset.</p> <p> </p> <h2>Updates:</h2> <p>- Update version 0.9: affects "Caravan_extension_CH" - In version 1.5 of the Caravan dataset, Penman-Monteith PET was added as an additional time series feature. Additional to the new time series feature, also all pet-related climate indices were recomputed using the new Penman-Monteith PET. For consistency, the old ERA5-Land potential_evaporation time series and climate indices were kept, but renamed for a better identification of the differences. </p> <p>- Update version 0.8: resolving projection issue for shapefiles in "Caravan_extension_CH" using EPSG:4326 (WGS84); updating readme file of "camels_ch" regarding the <a href="../communities/dischma/">Dischma</a> catchment</p> <p>- Update version 0.7: update corresponding to the revision of the manuscript at "Earth System Science Data" (ESSD)</p> <ul> <li>dataset file delimiters have been changed to commas from semicolons</li> <li>the "time_series" folder was renamed to "timeseries"</li> <li>in the simulation-based data, there was an error in the previous aggregation of precipitation and evapotranspiration. The corresponding time series, affected hydrologic signatures and climatic indices were corrected</li> <li>the order of simulation-based variables in the timeseries files was changed to resemble the order shown in the tables of the corresponding publication in ESSD</li> <li>blank values that were masked by "NA" are now consistently indicated by "NaN"</li> <li>the readme file has been extended</li> </ul> <p>- Update version 0.6: updating links to related material (all links and references are available in the preprint/manuscript) and abstract</p> <p>- Update version 0.5: adding the "camels_ch_data_description.pdf" file</p> <p>- Update version 0.4: update of several static attributes in "Caravan_extension_CH" following a general update in Caravan and all its extensions + adopting the geographic coordinate system to Caravan-standard EPSG:4326</p> <p>- Update version 0.3: renaming single files/entries in "Caravan_extension_CH" to start with "camelsch" as unique Caravan extension identifier</p> <p>- Update version 0.2: CH extension to <a href="../records/7944025">Caravan</a> added</p>
Data and code for: "Global Sampling Decline Erodes Science Potential of Natural History Collections"
<p># GBIF Specimen Data Analysis and Forecasting<br><br>## Version 2 - modified date ranges for figures 1 and 2 in response to reviewer comments</p> <p>This repository contains the code and data for analysing and forecasting trends in Global Biodiversity Information Facility (GBIF) specimen records across three major taxonomic groups: Chordata, Arthropoda, and Plantae. <br>The analysis pipeline includes data cleaning, anomaly detection, primary analyses, and forecasting based on historical database snapshots.</p> <p>These scripts and data correspond to analyses in the following manuscript:</p> <p>Global Sampling Decline Erodes Science Potential of Natural History Collections</p> <p>Authors:<br>Owen Forbes<br>Andrew G. Young<br>Peter H. Thrall</p> <p><br>## Repository Structure</p> <p>The repository consists of three main Quarto (.qmd) scripts and associated data files:</p> <p>1. `1_DataCleaning_Forbes-et-al_2025.qmd`: Data cleaning and anomaly detection<br>2. `2_PrimaryAnalyses_Forbes-et-al_2025.qmd`: Primary analyses and visualisation<br>3. `3_SnapshotsForecasting_Forbes-et-al_2025.qmd`: Historical snapshot analysis and forecasting</p> <p>## Requirements</p> <p>- R (version 4.3.2 or later)<br>- Required R packages:<br> - tidyverse (v2.0.0) - for data manipulation and visualization<br> - readr (v2.1.5) - for reading CSV/TSV files<br> - ggplot2 (v3.4.0 or v3.5.0) - for creating visualizations<br> - rnaturalearth (v1.0.1) - for accessing natural earth map data<br> - dplyr (v1.1.0 or v1.1.4) - for data manipulation<br> - countrycode (v1.6.0) - for converting country names and codes<br> - spdep (v1.3-3) - for spatial dependence modeling<br> - sp (v1.6-0 or v2.1-3) - for spatial data manipulation<br> - sf (v1.0-15 or v1.0-16) - for simple features access<br> - data.table (v1.14.8) - for fast aggregation of large data<br> - lubridate (v1.9.2) - for date-time manipulation<br> - viridis (v0.6.3) - for color palettes<br> - gridExtra (v2.3) - for arranging multiple plots<br> - ggpubr (v0.6.0) - for creating publication-ready plots<br> - zoo (v1.8-12) - for time series, including moving averages<br> - scales (v1.3.0) - for graphical scales<br> - forecast (v8.22.0) - for ARIMA forecast models<br> - purrr (v1.0.2) - for mapping custom forecast function onto each dataset<br> - arrow - for working with parquet files</p> <p>Install these packages before running the scripts.</p> <p>## How to Use</p> <p>1. Download this repository to your local machine.<br>2. Set your working directory to the location of the scripts.<br>3. Download raw datasets from GBIF (as required)<br>4. Ensure all required R packages are installed.<br>5. Run the scripts in RStudio or your preferred R environment.</p> <p>### Data Cleaning (`1_DataCleaning_Forbes-et-al_2025.qmd`)</p> <p>This script cleans the raw GBIF data and identifies anomalies. It produces files containing indexes of dataset records to be removed, which are used in subsequent analyses.</p> <p>**Note**: The raw GBIF exported datasets for contemporary records are not included in this repository due to file size constraints. Download them from the GBIF links provided in the script and place them in the `data/` directory.</p> <p>### Primary Analyses (`2_PrimaryAnalyses_Forbes-et-al_2025.qmd`)</p> <p>This script performs the main analyses and generates visualisations. It uses the outputs from the data cleaning script to filter anomalous records.</p> <p>To reproduce all analysis stages from the original raw .csv files:<br>- Start at the chunks labelled "DATA LOAD AND FILTERING".<br>- Run the pipeline for non-spatial analyses before spatial analyses.<br>- Due to memory constraints, it's recommended to run analyses for one taxonomic group and one analysis stream at a time.</p> <p>To skip to plot generation:<br>- Navigate to sections tagged as "@! SKIP TO PLOTTING !@".<br>- Ensure all required analysis output files are in the `data/` directory.</p> <p>### Forecasting (`3_SnapshotsForecasting_Forbes-et-al_2025.qmd`)</p> <p>This script analyses historical GBIF database snapshots and forecasts future growth. It uses the cleaned snapshot data produced by the data cleaning script.</p> <p>## Data Files</p> <p>### GBIF Exports - Raw Data (not included on Zenodo due to file size, please download directly from GBIF)<br>- `0016915-240425142415019.csv` for Chordata - https://www.gbif.org/occurrence/download/0016915-240425142415019</p> <p>- `0016914-240425142415019.csv` for Plantae - https://www.gbif.org/occurrence/download/0016914-240425142415019 </p> <p>- `0016913-240425142415019.csv` for Arthropoda - https://www.gbif.org/occurrence/download/0016913-240425142415019</p> <p>### Included Data Files</p> <p>#### Raw Data<br>- `GBIF_snapshots.parquet` # Historical snapshots RAW dataset (arrow/parquet format)<br>- `GBIF_integer_to_datasetKey.tsv` # Mapping old dataset IDs onto new datasetKey field</p> <p>#### Contemporary Datasets - data cleaning outputs<br>- `chordata_counts_to_highlight_030724` # List of anomalous Chordata dataset + year indexes to filter<br>- `arthropoda_counts_to_highlight_OG_030724` # List of anomalous Arthropoda dataset + year indexes to filter<br>- `plantae_counts_to_highlight_030724` # List of anomalous Plantae dataset + year indexes to filter</p> <p>#### Cleaned Snapshots<br>- `plantae_snapshots_filter_threshold_IN_040924` # Cleaned Plantae snapshots<br>- `arthropoda_snapshots_filter_threshold_IN_040924` # Cleaned Arthropoda snapshots<br>- `chordata_snapshots_filter_threshold_IN_040924` # Cleaned Chordata snapshots<br>- `gbif_dates_df_anomaly_filtered_090724` # Anomaly-filtered snapshots (combined dataset)<br>- `gbif_dates_df_anomalies_highlighted_090724` # Anomalies highlighted snapshots (combined dataset)</p> <p>#### Analysis Outputs - for skipping straight to plot/figure generation<br>- `arthropoda_specimens_per_year_080724` # Arthropoda specimen counts per year<br>- `arthropoda_unique_species_per_year_080724` # Arthropoda unique species counts per year<br>- `arthropoda_grid_counts_080724` # Arthropoda grid counts<br>- `chordata_specimens_per_year_080724` # Chordata specimen counts per year<br>- `chordata_unique_species_per_year_080724` # Chordata unique species counts per year<br>- `chordata_grid_counts_080724` # Chordata grid counts<br>- `plantae_specimens_per_year_080724` # Plantae specimen counts per year<br>- `plantae_unique_species_per_year_080724` # Plantae unique species counts per year<br>- `plantae_grid_counts_080724` # Plantae grid counts<br>- `chordata_continent_count_080724` # Chordata continent-specific counts<br>- `arthropoda_continent_count_080724` # Arthropoda continent-specific counts<br>- `plantae_continent_count_080724` # Plantae continent-specific counts</p> <p> </p>
Challenges of sampling and how phylogenetic comparative methods help: Supplementary data
<p>Supplementary data and results files for the paper:</p> <p>Macklin-Cordes, Jayden L. & Erich R. Round (2022). Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrast. <em>Linguistic Typology</em> (advance online publication). <a href="https://doi.org/10.1515/lingty-2021-0025">https://doi.org/10.1515/lingty-2021-0025</a></p>
Fig. 57. Solmundella bitentaculata, sample BFLA4119, bell diameter 4 in Hydromedusae observed during night dives in the Gulf Stream
Fig. 57. Solmundella bitentaculata, sample BFLA4119, bell diameter 4 mm. (A-B) Lateral views of whole animal, note the green mouth region. (C) Aboral view.
MPIData: Optimized System Matrix Sampling
<p>This data repository contains magnetic particle imaging data. The files are stored in the <a href="https://arxiv.org/pdf/1602.06072.pdf">MDF file format</a> and the folder structure is organized as an <a href="https://github.com/MagneticParticleImaging/MPIFiles.jl">MDF store</a>. The data is used in this code example: <a href="https://github.com/IBIResearch/IncreasedDynamicRange">https://github.com/IBIResearch/IncreasedDynamicRange</a></p>
Sampling bias exaggerates a textbook example of a trophic cascade
<p>Understanding trophic cascades in terrestrial wildlife communities is a major challenge because these systems are difficult to sample properly. We show how a tradition of nonrandom sampling has confounded this understanding in a textbook system (Yellowstone National Park) where carnivore [<em>Canis lupus</em> (wolf)] recovery is associated with a trophic cascade involving changes in herbivore [<em>Cervus canadensis</em> (elk)] behavior and density that promote plant regeneration. Long-term data indicate a practice of sampling only the tallest young plants overestimated regeneration of overstory aspen (<em>Populus tremuloides</em>) by a factor of 3-8 compared to random sampling because it favored plants taller than the preferred browsing height of elk and overlooked non-regenerating aspen stands. Random sampling described a trophic cascade, but it was weaker than the one that nonrandom sampling described. Our findings highlight the critical importance of basic sampling principles (e.g., randomization) for achieving an accurate understanding of trophic cascades in terrestrial wildlife systems.</p>
Use of infrared spectroscopy for a sampling study of waste wood samples in a panel board industry
<p>Oral presentation at the conference 'NIR Italia Online, waiting for Slovenia 2022' (24-25 February 2021). </p> <p>NIR Italia Symposium are biennial conferences on infrared spectroscopy. Due to the pandemic situation the Italian Society for Near Infrared Spectroscopy (SISNIR), in collaboration with the InnoRenew CoE and University of Primorska, has decided to organize an online National Symposium of NIR Spectroscopy, waiting for the opportunity to meet physically next year. </p> <p>The event is an important opportunity to virtually present works, and exchange ideas, opinions and future perspectives. This presentation perfectly fits with theme of NIR spectroscopy. Indeed, the presentation is about the use of NIR spectroscopy for assessing the best sampling procedure in order to describe a really heterogeneous material as waste wood is. </p>
Bridging the Gap between Autonomous and Predetermined Paradigms: The Role of Sampling in Evaluative Learning
<p>While most evaluative learning paradigms remove participants’ autonomy over the information they receive, other research traditions have demonstrated that information sampling has an important role in learning. We investigate the impact of information sampling on a central evaluative learning paradigm: evaluative conditioning. We compare a traditional evaluative conditioning paradigm with a paradigm in which participants have autonomy over the stimulus pairings they receive. Participants in the high-autonomy condition show a strong preference for positively paired CSs. Nevertheless, the strength of evaluative conditioning effects was independent of autonomy. Moreover, high-autonomy participants, but not their low-autonomy counterparts, demonstrate a relationship between sampling frequency and evaluations, in line with the interpretation that sampled stimuli become more positive, whereas ignored stimuli become more negative over the course of the learning phase. The present research provides a cornerstone for integrating several research traditions within and beyond the evaluative learning literature, providing a foundation for new insights and more comprehensive theories of evaluative learning.</p>
Supporting data (structural data, sample descriptions, microprobe data)
<p>Supporting data for the manuscript entitled "Tectonic evolution of the Nevado-Filábride complex (Sierra de los Filábres, Southeastern Spain): insights from new structural and geochronological data", submitted to Tectonics.</p>
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.