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109 results for “year 2020”
Dataset accompanying Riesch et al. 2020. Grazing by wild red deer maintains characteristic vegetation of semi-natural open habitats: Evidence from a 3-year exclusion experiment. Applied Vegetation Science
<p>This repository contains vegetation community data used by Riesch et al. 2020 in an article accepted in Applied Vegetation Science.</p> <p>Metadata are provided in the first excel worksheet. For further details please see the original article.</p>
Data and code - Reproducibility improves exponentially over 63 years of research - Minocher et al. 2020
<p>Data and code to reproduce analyses in the publication - Minocher, et al. "Reproducibility improves exponentially over 63 years of social learning research". </p> <p>This repository is maintained at github https://github.com/rianaminocher/reproducibility-analysis.</p>
Butuan City, Philippines Population Grids for the Year 2020 with DEGURBA Classification
<p>Population grids of Butuan City, Philippines for the year 2020, in Esri Shapefile and TIFF (raster) formats.</p> <p>The population grids were generated through disaggregation of barangay-level population data to square grid cells (e.g., 1km x 1km), following the method presented in [1], using building footprints data (Google Open Buildings) as a covariate. In detail, the total population of each census unit (barangay) is proportionally distributed across its building footprints, based on the assumption that the presence of building footprints correlates with population distribution. The population within each grid cell is then calculated by summing the proportionally distributed population from the building footprints within that cell. Before implementing the disaggregation procedure, minor cleaning of the building footprint data was conducted. This involved removing building polygons with an area of less than 6 m², as buildings below this size are assumed to be uninhabitable. This assumption is based on the minimum room size requirements outlined in the National Building Code of the Philippines (PD 1096).</p> <p>The 1km x 1km Shapefile contains two main attributes ('pop2020' and 'degurba'): 'pop2020' is the estimated population at each 1km x 1km grid cell for the year 2020; 'degurba' corresponds to the classification of each grid cell based on the DEGURBA (Degree of Urbanisation) classification methodology [1].</p> <p>For the raster version of the data, the pixel values correspond to the total population in that pixel.</p> <p>Data sources:</p> <ul> <li>Building footprints © 2021 Google Open Buildings dataset, licensed under ODbL 1.0, <a href="https://sites.research.google/open-buildings/" target="_blank" rel="noopener">https://sites.research.google/open-buildings/</a></li> <li>Population Data © 2020 Philippine Statistics Authority (PSA), <a href="https://psa.gov.ph/content/2020-census-population-and-housing-2020-cph-population-counts-declared-official-president" target="_blank" rel="noopener">https://psa.gov.ph/content/2020-census-population-and-housing-2020-cph-population-counts-declared-official-president </a></li> <li>Barangay Boundaries © 2023 PSA and NAMRIA via OCHA Centre for Humanitarian Data - HDX, <a href="https://data.humdata.org/dataset/cod-ab-phl" target="_blank" rel="noopener">https://data.humdata.org/dataset/cod-ab-phl?</a></li> </ul> <p>Reference</p> <p>[1] Applying the Degree of Urbanisation A METHODOLOGICAL MANUAL TO DEFINE CITIES, TOWNS AND RURAL AREAS FOR INTERNATIONAL COMPARISONS 2021 Edition, <a href="https://dx.doi.org/10.2785/706535" target="_blank" rel="noopener">https://dx.doi.org/10.2785/706535</a></p>
Akko - Year of the Rat 2020
This is something I made a few months ago, it started as an exercise for 3D props design. I though this character from the Little Witch Academia anime series would fit perfectly for a chinese traditional plate style as I could find in examples from my research. Source: Objaverse 1.0 / Sketchfab
50 Years of Art Song & Gender on Major European Stages, Part 1 (Austria): Vienna's Konzerthaus, Musikverein & Salzburg Festspiele Song Recitals 1970 - 2020
<p>3 .csv files containing names, years and gender breakdown (m=male, f=female, b=both) of pianists, singers, and composers featured vocal recitals on the three most prestigious stages in Austria, the Musikverein an Konzerthaus in Vienna and at the Salzburger Festpiele between 1970 and 2020.</p> <p>Data was scraped by the author from the Festspiele digital archive (https://archive.salzburgerfestspiele.at/archiv), the concert archives of the Gesellschaft der Musikfreunde in Wien (https://www.musikverein.at/en/archive/) and the Konzerthaus databank (https://konzerthaus.at/datenbanksuche) and is part of an ongoing project to visibilize the gendered nature of collaborative piano at the most prestigious levels. Thanks to Diána Fuchs (https://diana-fuchs.com/) who assisted with the collection of the Musikverein data, with the support of Melanie Unseld at the Department of Musicology and Performance Studies at the mdw - University of Music and Performing Arts Vienna.</p>
Ground Truth Data of Falcata (Paraserianthes falcataria) Trees and Tree Farms/Plantations in Butuan City, Philippines for the Year 2020
<p>This dataset comprises location and extent data of<strong> </strong>selected/visited Falcata (<em>Paraserianthes falcataria</em>) trees and tree farms/plantations in Butuan City, Philippines. The CSV and GIS Shapefiles were generated from data collected through a series of ground truth (field) surveys conducted from October 20-29, 2020, in various parts of the city. More details can be found in the accompanying technical report. The field surveys were conducted as part of the <a href="../communities/nicer-itp-project-1" target="_blank" rel="noopener">CSU NICER ITP Center - Project 1. Development of a Geodatabase of Industrial Tree Plantations In Caraga Region Using Remote Sensing and GIS.</a></p> <p>Please refer to the Project 1 terminal report (<a href="../records/13735736" target="_blank" rel="noopener">https://zenodo.org/records/13735736</a>) for additional details.</p>
Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".
<p>This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".<br>The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.</p>
Acoustic Current Profiler data from Multi-Year (2020-2022) Autonomous Underwater Glider Surveys in the Anegada Passage
<p>This dataset contains glider acoustic doppler profiler observations from four glider deployments in the Anegada Passage region from 2020-2022. The 2020-2021 data are from a Nortek AD2CP and the 2022 data are from a Teledyne RDI Pathfinder. The RDI Pathfinder .PD0 files can be read directly in the code that is used for this analysis. The Nortek AD2CP .ad2cp files are processed using Nortek's MIDAS software to generate NetCDFs. <br> </p>
Time-to-Event analysis of factors influencing delay in discharge from a subacute Complex Discharge Unit during the first year of the pandemic (2020) in an Irish tertiary centre hospital
<p><strong>Figure S1:</strong> Forest plots 1 and 2 depicting Age and Gender strata associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited statistically significant results for individuals <65 years of age who had a delay in discharge due to complications from comorbidities; those in 65-75 years of age category, had prolonged LOS due to admission with frailty, falls and/or integrated rehabilitation needs; and 75-85 years of age category showed an association of at least 4 out of the 5 common delaying factors. Strata Gender exhibited a significant delay in discharge due to complications from comorbidities and patient-centred needs; in comparison to the female gender who also experienced a delay in discharge as a result of both factors alongside frailty, falls and/or integrated rehabilitation needs.<strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community services]. </p> <p><strong>Figure S2:</strong> Forest plot 3 depicting Multimorbidity (MM) strata-associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited a significant delay in discharge due to complications from comorbidities, frailty, falls, and/or integrated rehabilitation and patient-centred needs in patients with ≤4 MM. In contrast patients with >4 MM experienced significant delays in discharge due to complications from comorbidities and patient-centred needs. <strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community Services].</p>
Hourly wind profile measurements at Beijing weather station for the year 2020
<p>This dataset contains the wind profiler from 0.14 to 10.38 km above ground level at 1-h intervals for the Beijing Observatory station, which is obtained from the measurements of the Radar wind profiler for the year 2020. It is stored in Matlab format, and is organized by a matrix of 21*8784. the field "h" represents its corresponding height.</p>
AROME forecasts of surface shortwave downward radiation for year 2020
<p>This dataset contains hourly AROME forecasts (24-hour forecasts starting at 00:00 UTC) of surface shortwave downward radiation (SWD), across the whole AROME and for year 2020. This dataset was used to evaluate AROME SWD forecasts in the manuscript "Evaluation of surface surface shortwave downward radiation forecasts by the numerical weather prediction model AROME" by Marie-Adèle Magnaldo et al., submitted to Atmospheric Chemistry and Physics.</p>
Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach
<p>Daily gridded lake surface temperature (LST) data (1979-2020) for each Great Lake - Superior (GLS), Michigan (GLM), Huron (GLH), Erie (GLE) and Ontario (GLO) - derived from LSTM detailed in Kayastha et al. (2023) paper: "Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach".</p> <p>Each matfile contains longitude (lon), latitude (lat), as well as the LST for each grid point. The files also contain the variable 'art1' (Area of Node-Base Control volume) required to calculate lake-wide average LST. The depth at each location (dep) is also provided.</p>
Hyytiälä SMEAR II forest year 2020 thinning tree and carbon inventory data
<p>This dataset contains description of thinning, and forest and carbon stock inventory results of the forest stand surrounding SMEAR II research station located in Hyytiälä, Finland.</p> <p>The dataset consists of following files:</p> <p>1. Description of thinning and inventory methods (Hyytiala site description.pdf)</p> <p>2. Forest characteristics before and after the thinning, by measurement plot (Forest characteristics.xlsx)</p> <p>3. Carbon stocks before and after the thinning, as well as carbon stocks in commercial removal, by measurement plot (Carbon stocks.xlsx)</p> <p>4. Equations used for procuding estimates presented in products 2&3 (Equations.xlsx)</p> <p>5. Readme-file explaining details regarding variants in products 2&3 (Readme.xlsx)</p> <p>6. Plot map showing the locations of measurement plots (Plot map.xlsx)</p> <p>7. Tree size distribution before and after the thinning (Tree size distribution.xlsx)</p> <p>8. Cleaned tree-wise inventory data (Plot measurement data 2020.xlsx) from</p> <ul> <li> 2019 understorey measurement results</li> <li> 2020 measurement results</li> <li> Plot 1-24 detailed inventory measurement results</li> </ul>
Areas of low natural regeneration potential post-fire in shrublands of southern California (selected years between 2008 and 2020)
Open the record for dataset details and reuse information.
Resurveying breeding forest bird communities in Western Oregon after 50 years: comparing 1968–1970 and 2020–2021
Open the record for dataset details and reuse information.
Datasets of the ZKI Top Trends Survey of the ZKI Working Group Strategy and Organisation for the Year 2020
<p>The Working Group Strategy and Organisation of the ZKI Association conducts an annual survey on the most important topics and focal points of the member institutions. In 2016 the survey was changed to an online survey using LimeSurvey. For the year 2020, the survey was expanded with an additional catalogue of questions on the focus "digitisation". The main catalogue of 12 core questions is standardised each year and consists of free text questions. The answers are categorized and normalised for this analysis. </p> <p>This file contains raw data for the survey.</p>
Supplementary material 1 from: Yeung NW, Slapcinsky J, Strong EE, Kim JR, Hayes KA (2020) Overlooked but not forgotten: the first new extant species of Hawaiian land snail described in 60 years, Auriculella gagneorum sp. nov. (Achatinellidae, Auriculellinae). ZooKeys 950: 1-31. https://doi.org/10.3897/zookeys.950.50669
Non-type material examined for Auriculella auricula, A. minuta, A. perpusilla, A. perversa, and A. tenella
Measuring the disease burden of seasonal influenza in Germany 2015 – 2020 using the incidence-based disability-adjusted life years (DALYs)
<p>Version 1.0.0 was created. Dataset includes all parameters and values used for all scenario-specific calculations, using the Burden of Communicable diseases in Europe (BCoDE) tool.</p>
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2020
<div> <div> <div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2020</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div> </div> </div>
Copernicus EMS flood activations delimitations (2012 - 2020) rasterised at 30m and aggregated per year and season
<p>This dataset was created as part of the <a href="https://opendatascience.eu/">Geo-harmonizer project</a>, with the scope of making open data easier to access. It contains all the flood activations mapped by the <a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">Copernicus Emergency Rapid Mapping Service</a> between 2012 and 2020. To obtain these GeoTIFFs, the vector data packages from CEMS were individual downloaded, rasterized and mosaicked per year and season, resampled at 30-m and reprojected to <a href="https://epsg.io/3035">EPSG 3035: ETRS89-extended / LAEA Europe</a>. If no CEMS flood activation was identified in a specific year and season, the raster was not created. The rasters are provided as COG files, type=16Int, the flooded area pixels have value 100, nodata value is 255.</p> <p>To allow an easier and faster search through all 2012 - 2020 CEMS flood activations, we have prepared a point vector layer (geojson) containing one point for each flood activation area of interest with the following attributes attached: CEMS identification number <ems_id>, area of interest defined by CEMS <ems_aoi>, URL link to the CEMS activation <ems_link>, year of the event <year_start>, <year_end> , <season> and the name of the <geo_harmonizer_raster> where the 30m rasterised delimitations of the flooded areas of the corresponding flood activation can be found. </p> <p>For any additional questions regarding the data, please contact the author at codrina.ilie[at]terrasigna.com.</p> <p>The Copernicus Emergency Rapid Mapping Service data access policy is available <a href="https://emergency.copernicus.eu/mapping/sites/default/files/files/CopernicusEMS-Data_and_Dissemination_Policy.pdf">here</a>.</p>
ScienceDex guides
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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.