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410 results for “Data Repositories”
Data Repository for: SOCIO-ENVIRONMENTAL IMPACTS OF OIL PALM CONTRACT FARMING SCHEMES IN THE BRAZILIAN AMAZON
<p>Contract farming is arguably a pro-poor strategy to promote rural development and minimize the social impacts of large-scale agricultural expansion. Yet, little attention has been paid to its environmental impacts, particularly in tropical landscapes. This article fills this gap by linking social and environmental analysis of oil palm contract farming in the Brazilian Amazon. The analysis presented used a mix of quantitative and qualitative methods, including household surveys, remote sensing techniques, and in-depth interviews, to assess whether the scheme managed to avoid deforestation and to contribute to livelihood improvements. The results show that the Brazilian model managed to prevent the deforestation of primary forests, but achieved limited and differentiated livelihood results. The analysis suggests that the Brazilian model is more likely to work for households with an agricultural vocation and a commercial spirit in areas with an abundant availability of degraded lands, but can hardly be a solution in frontier areas or for subsistence or more dependent households. The chapter concludes with some reflections on how contract farming schemes should be designed in order to maximize livelihood gains and minimize negative environmental impacts.</p>
Grass Phylogeny Working Group III: data repository
<p><strong>Grass Phylogeny Working Group III: data repository</strong></p> <p>Phylogenetic analyses of the grass family (Poaceae) using nuclear and plastid data. The data set includes 1153 accessions corresponding to 1133 accepted species. Genomic data was obtained from different sources including target capture, shotgun, transcriptomes and annotated genomes. Nuclear markers (Angiosperm353 gene set) were assembled from short read data using HybPiper or a custom assembly pipeline optimized for low coverage shotgun data. Plastid genes were either retrieved from published plastome sequences or assembled here using getOrganelle. This data set also includes the results of a gene tree-species tree reconciliation analysis using GeneRax.</p> <p> </p> <p>Contact persons:</p> <p>Matheus E. Bianconi (matheus-enrique.bianconi@univ-tlse3.fr), Jan Hackel (jan.hackel@uni-marburg.de), Maria S. Vorontsova (m.vorontsova@kew.org)</p> <p> </p> <p>Content description</p> <p><strong>1. Metadata</strong></p> <ul> <li><code>gpwgIII_samples_metadata_taxonomy.tsv</code></li> </ul> <p>Tab-separated file with details for all 1,702 accessions used in this study. Columns: analysis_ID - ID in nuclear analyses; analysis_ID_plastome - ID in plastome analyses; acc_species - accepted species name; acc_species_author - taxonomic species authority; acc_genus - accepted genus name; acc_genus_author - taxomomic genus authority; publication - associated prior publication; data type - type of sequence data; isolate - laboratory isolate ID; voucher_ID - herbarium voucher ID; germplasm_ID - germplasm collection ID; repo_accession - accession number in public repository; plastome_accession - accession number of assembled plastome sequence; removed_nuclear - reason for removal from nuclear tree, if applicable; removed_plastome - reason for removal from plastome tree, if applicable; soreng2022_genus - genus name in Soreng et al. 2022, https://doi.org/10.1111/jse.12847; subtribe, tribe, subfamily, major.clade - classification according to Soreng et al. 2022.</p> <p><strong>2. Nuclear data</strong></p> <p><em>- Dataset1 ("main")</em><br>Number of samples: 1153<br>Number of genes: 331<br>Alignment trimming threshold: gt = 0.1 (removed sites > 90% missing data)<br>Genes per sample: > 166</p> <p><em>- Dataset2 ("strict trimming")</em><br>Number of samples: 1153<br>Number of genes: 315<br>Alignment trimming threshold: gt = 0.5 (removed sites > 50% missing data)<br>Genes per sample: > 158</p> <p><em>- Dataset3 (dataset 1 without shotgun samples)</em><br>Number of samples: 841<br>Number of genes: 331<br>Alignment trimming threshold: gt = 0.1 (removed sites > 90% missing data)<br>Genes per sample: > 166</p> <p><strong>2.1. Raw sequences</strong></p> <p>Raw Ang353 sequence assemblies for all samples (pre-trimming and filtering)</p> <ul> <li><code>raw_Ang353_sequences.zip</code></li> </ul> <p><strong>2.2 Nuclear gene alignments</strong></p> <p>Trimmed alignments from datasets 1, 2 and 3.</p> <ul> <li><code>alignments_dataset1_main_final.zip</code></li> <li><code>alignments_dataset2_strict_trimming_final.zip</code></li> <li><code>alignments_dataset3_no_shotgun_final.zip</code></li> </ul> <p><strong>2.3. Nuclear gene trees</strong><br>Gene trees inferred using RAxML (GTRCAT, 100 bootstraps) for the alignments from datasets 1, 2 and 3.</p> <ul> <li><code>gene_trees_dataset1_main_final.zip</code></li> <li><code>gene_trees_dataset2_strict_trimming_final.zip</code></li> <li><code>gene_trees_dataset3_no_shotgun_final.zip</code></li> </ul> <p><strong>2.4. Multigene species trees</strong><br>Multigene species trees obtained using Astral-Pro3 from gene trees for datasets 1, 2 and 3. </p> <ul> <li><code>astralpro_trees.zip</code>, which includes: <ul> <li>trees_Ang353_grasses_dataset1_main_gtrcat.astralpro</li> <li>trees_Ang353_grasses_dataset2_strict_trimming_gtrcat.astralpro</li> <li>trees_Ang353_grasses_dataset3_no_shotgun_gtrcat.astralpro</li> </ul> </li> </ul> <p><strong>3. Gene tree–species tree reconciliation</strong></p> <ul> <li><code>generax.zip</code></li> </ul> <p>Compressed zip archive with input files and results, including log files, of the GeneRax reconciliation analysis. One subfolder for each of the four analyses run: "all_tribes", "Andropogoneae", "Bambusoideae", "Triticeae".</p> <ul> <li><code>transfers_reconciliation_analyses.zip</code>, which includes: <ul> <li>transfers_all_all_tribes.tsv: Tab-separated file with all transfers inferred with the tribe-level Poaceae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_all_Andropogoneae.tsv: Tab-separated file with all transfers inferred with the Andropogoneae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_all_Bambusoideae.tsv: Tab-separated file with all transfers inferred with the Bambusoideae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_all_Triticeae.tsv: Tab-separated file with all transfers inferred with the Triticeae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_counts_all_tribes.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the tribe-level Poaceae reconciliation analysis.</li> <li>transfers_counts_Andropogoneae.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the Andropogoneae reconciliation analysis.</li> <li>transfers_counts_Bambusoideae.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the Bambusoideae reconciliation analysis.</li> <li>transfers_counts_Triticeae.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the Triticeae reconciliation analysis.</li> </ul> </li> </ul> <p><strong>4. Plastome data</strong></p> <p>Alignment and phylogenetic tree from plastome data.</p> <ul> <li><code>plastome_files.zip</code>, which includes <ul> <li>reduced_plastome_concat_CDS_trnLtrnF_trimmed.fna-out.fas: FASTA file with the final, concatenated DNA alignment of 71 plastome regions for 910 accessions, after data filtering.</li> <li>partitions.txt: Text file with positions of the 71 plastome regions in the concatenated alignment.</li> <li>plastome_concat_CDS_trnLtrnF_trimmed_TBE.raxml.support: Plastome tree with Transfer Bootstrap Expectation values as node labels.</li> <li>RAxML_bipartitions.plastome_concat_CDS_trnLtrnF_trimmed: Maximum likelihood plastome tree inferred with RAxML, with Felsenstein bootstrap values as node labels.</li> <li>RAxML_bootstrap.plastome_concat_CDS_trnLtrnF_trimmed: 100 rapid bootstrap pseudoreplicate plastome trees inferred with RAxML.</li> <li>RAxML_info.plastome_concat_CDS_trnLtrnF_trimmed: RAxML analysis log file.</li> <li>nuc_plastome_matching_tips.tab: Tab-separated file with accessions matched in nuclear-plastome comparison.</li> </ul> </li> </ul> <p><strong>5. Poaceae-specific reference Ang353 dataset</strong><br>Reference sequence dataset used for the assembly of Ang353 sequences in this study.</p> <ul> <li><code>target_Ang353_sequences_grasses.zip</code></li> </ul> <p><strong>6. Shotgun assembly script</strong></p> <p>Custom script used for the assembly of Ang353 sequences from shotgun data</p> <ul> <li><code>shotgun_assembler_script.zip</code>, which includes: <ul> <li>shotgun_assembler_Ang353_sequences.sh: script for assembly of short reads from shotgun data</li> <li>template_manifest_file.tsv: TAB-separated file to specify sample names and location of short read files (required by the assembly script)</li> <li>list_Ang353_genes_orthofinder.txt: list of Ang353 gene identifiers (required by the assembly script)</li> </ul> </li> </ul> <p><strong>7. Quartet metrics script</strong></p> <p>R script to calculate the Quartet Concordance (QC) and Quartet Differential (QD) metrics from the gene tree frequencies/proportions for each quartet at a branch, following Pease et al. 2018 (American Journal of Botany, <span><a href="https://doi.org/10.1002/ajb2.1016" target="_blank" rel="nofollow noopener noreferrer">https://doi.org/10.1002/ajb2.1016</a></span>).</p> <ul> <li><code>quartet_metrics.R</code></li> </ul>
Compilation of data collected in surveys on the WissKI-based 3D Repository with DFG 3D-Viewer project partners and architecture students from the Warsaw University of Technology and the Technical University of Łódź
<p>The dataset contain the compiliation of responses from users of WissKI-based 3D Repository (https://3d-repository.hs-mainz.de/)., which is the open platform for deposit of 3D models of cultural heritage. The beta version of the WissKI 3D Repository, initiated in June 2022, has been subjected to evaluation by two primary target groups since its launch. The initial group, composed of students in the cultural heritage domain, was tasked with showcasing the importance of documenting and publishing 3D models of digital reconstructions. The survey with sutdents was conducted for three different classes: </p> <p>1) In summer 2022 with bachelor architectrue students at Warsaw University of Technology during seminar of choice regarding digital reconstruction of wooden synagogues;</p> <p>2) In summer 2023 with bachelor architectrue students at Warsaw University of Technology, and master students from Technology University of Łódź during seminar of choice regarding digital reconstruction of wooden synagogues;</p> <p>3) In autumn 2023 during international workshop about digital 3D heritage of CoVHer project with studnets of architecture from Warsaw Univeristy of Technology, Alma Mater Studiorum – Universita di Bologna, Facoltà di Architettura di Porto and Hochschule Mainz - University of Applied Sciences, as well as archaeology studnets from Universitat Autònoma de Barcelona.</p> <p>The second group, comprising digital 3D cultural heritage professionals, predominantly focused on archiving digital assets. Participants were project partners of DFG 3D Viewer project, which were professionals from the Institute of Archaeology at University Cologne, the Institute of Art History at the Ludwig-Maximilians-Universität Munich, the Architecture, Civil Engineering and Urban Planning Department of BTU Cottbus Senftenberg, and the Detushce Museum. They were asked for evaluaton of system after three differetn stages of work: at the begging wihtout any introduction to the system, after proivision of intorudctionary materilas and finally at the end of work.</p> <p>All participants were requested to report their experiences across four categories: metadata form, 3D viewer, provided guidelines, and overall experience. A 5-point rating scale was employed to assess specific issues, with 1 being the most negative and 5 being the most positive. The form length question was an exception, where a median value of 3 was considered ideal, and extreme values indicated either excessive length or brevity.</p>
AgroRadarEval - Data and Code Repository.
<p>Contains the data, code, and supplementary materials associated with the article "Data-driven RD&I management for societal impacts through evaluation results: introducing and applying AgroRadarEval". AgroRadarEval aims to support leaders and managers in agricultural RD&I by reflecting on organizational capacities, culture, collaborations, processes, and communications underlying the use of evaluation results. The approach incorporates principles from Responsible Research and Innovation (RRI) and Responsible Research Assessment (RRA) to ensure that evaluation processes are inclusive, transparent, and aimed at societal impact.</p>
EPSRC HEED Data Repository: Nepal Household Appliance Survey
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand the energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, three Appliance surveys were conducted in the Uttargaya settlement in Nepal. Appliance surveys are designed to assess the energy needs of a community based on the devices they use. The surveys span three instances across 18 months, starting in October 2018 and ending in April 2020.</p> <p>The survey splits the participants into four categories, organised into sheets, based on the type of metering participants have: 'bulk meter'; 'sub meter'; 'do not possess meter' and 'do not have electrical connection'. The survey anonymises the name of participants and assigns them a unique id as a household number. Information is recorded on the gender of the household owner, the number of people in the household, the type of their electricity connection and the payment type for the electricity connection. The survey collects information on how many of the following appliances have: Electric Bulb; Mobile charger; Refrigerator; Television; Electric Radio; Table Fan; Electric Iron.</p>
Repository Analytics and Metrics Portal (RAMP) 2021 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2021. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p> <p>The record will be revised periodically to make new data available through the remainder of 2021.</p>
Data Repository - Thermal-electrochemical parametrisation of a lithium-ion battery: mapping Li concentration and temperature dependencies
<p>Datasets from "Thermal-electrochemical parametrisation of a lithium-ion battery: mapping Li concentration and temperature dependencies" - Journal of Electrochemical Society.</p> <p>This repository contains parameter values for the electrode solid-state diffusivity, entropic term, exchange current density, electronic conductivity, specific heat capacity, and thermal conductivity.</p>
Binary Classification as a Phase Separation Process (data repository)
<p><strong>For version 0.0.2 (from 2021) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>This is a second version, which I wrote using tensorflow. It is much smaller (5 Gb when decompressed), a remarkable improvement when compared to the more than 100 Gb of the previous version).</p> <p>The new files are </p> <ul> <li>PSBC_BCs.tar.gz</li> <li>PSBC_classifier_PCA.tar.gz</li> <li>PSBC_dataset.tar.gz</li> <li>PSBC_libs_grids_statistics.tar.gz</li> <li> PSBC_notebooks.tar.gz</li> </ul> <p>Their content is explained in the file README_v2.pdf</p> <p><strong>UPDATE: <a href="https://drive.google.com/drive/folders/18l_92HuHDWJDkZnvXRuyGedcyC_3YZ2M?usp=sharing">a Google Colab folder is also available</a>. You can also find all the data and libraries there, unpacked.</strong></p> <p>For usage, see the Git-hub. </p> <blockquote> <p><strong>NOTE)</strong> I will keep the content for the previous version available in my Github as well. It is still a "nice exercise" to do all that is done in this new version in numpy, as done there. <strong><em>(Or, I should say, they should be studied as a cautionary tale of what to avoid.)</em></strong></p> </blockquote> <p> </p> <p><strong>For version 0.0.1 (from 2020) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>Therein you will find</p> <ul> <li>Examples</li> <li>1D toy model examples</li> <li>Computational statistics</li> <li>Several trained PSBC on MNIST dataset, with different parameter configurations</li> <li>Extra simulations, investigating normalization properties, low dimensional models that fail due to "too much" model compression, and comparison among ANNs, KNNs, and the PSBC in 1D</li> </ul> <p>If you want to know</p> <ol> <li>how to read the data</li> <li>how to access computational statistics, raw data, and examples</li> <li>how to use the data stored in this data repository</li> </ol> <p>see the guide README.pdf on GitHub page at <a href="https://github.com/rafael-a-monteiro-math/Binary_Classification_Phase_Separation">Binary_Classification_Phase_Separation</a>, where a script that downloads (and organizes) all this data is also available ("download_PSBC.sh).</p> <p>I did not include a copy of the train-test set (0-1dubset of the MNIST database) in every folder with simulations. But you can find a copy of the normalized dataset in the tar ball "PSBC_Examples.tar.gz" as</p> <p>data_test_normalized_MNIST.csv and data_train_normalized_MNIST.csv.</p> <p> </p>
Madagascar's extraordinary biodiversity: a data repository
<p>Data repository for the two sister reviews of Madagascar's biodiversity:</p> <ul> <li>Antonelli et al.: "Madagascar's extraordinary biodiversity: Evolution, distribution, and use", Science 378 (6623): eabf0869 – <a href="https://doi.org/10.1126/science.abf0869">https://doi.org/10.1126/science.abf0869</a></li> <li>Ralimanana et al.: "Madagascar's extraordinary biodiversity: Threats and opportunities", Science 378 (6623): eadf1466 – <a href="https://doi.org/10.1126/science.adf1466">https://doi.org/10.1126/science.adf1466</a></li> </ul> <p>Please note the author order of this data repository is different from the author order and contributions in the review papers.</p> <p>REVIEW I: EVOLUTION, DISTRIBUTION AND USE</p> <ul> <li>catalogue_of_vascular_plants_of_madagascar.csv -- Comma-separated table with comprehensive taxonomic database of vascular plants of Madagascar, from the Catalogue of the Vascular Plants of Madagascar project. Contact: Peter Phillipson - peter.phillipson@mobot.org and Marina Rabarimanarivo - marina.rabarimanarivo@mobot.mg</li> <li>fungi_supplementary_material.zip -- Zipped archive containing: R script to get fungal endemism estimates from GBIF/UNITE and process data; comma-separated tables from GBIF, PlutoF, Goodman lichen checklist and Index Fungorum (as of 02/12/20): comma-separated table listing Madagascan taxa with endemism status. Contact: Rowena Hill - r.hill@kew.org</li> <li>lineages_madagascar.csv -- Comma-separated table with crown and stem ages, number of species, and geographic origin or distribution of sister clade of Malagasy endemic lineages, extracted from the literature. Contact: Jan Hackel - j.hackel@kew.org and Angelica Crottini - tiliquait@yahoo.it</li> <li>madagascar_fossil_genera_occurrences.csv -- Comma-separated table with worldwide fossil records for Malagasy species, downloaded from the PaleoBiology Database. -- Contact: Juan Carillo - juan.carrillo@mnhn.fr</li> <li>species_richness_modeling_occurrences.csv -- Comma-separated table with specimen-based occurrence data for Malagasy amphibians, grasses, lemurs, palms, reptiles, and Sarcolaenaceae. Contact: Weston Testo - westontesto@gmail.com</li> <li>taxon_description_by_year.csv -- Comma-separated table with years of basionym publication for Malagasy amphibians, reptiles, vascular plants, and ants. Contact: Weston Testo - westontesto@gmail.com</li> <li>vegetation_moatsmith_1km_extended.zip -- Raster geotiff with new expanded vegetation types based on Moat & Smith (2007). Contact: Justin Moat - j.moat@kew.org</li> <li>vertebrate_species_list.csv -- Comma-separated table with native species list and associated endemism of freshwater fishes, amphibians, reptiles, birds, and mammals of Madagascar (author-curated list based on data from The New Natural History of Madagascar and the IUCN Red List). Contact: Weston Testo - westontesto@gmail.com, Angelica Crottini - tiliquait@yahoo.it, Ferran Sayol - fsayol@gmail.com</li> </ul> <p>REVIEW II: THREATS AND OPPORTUNITIES</p> <ul> <li>catalogue_of_vascular_plants_of_madagascar.csv -- Comma-separated table with comprehensive taxonomic database of vascular plants of Madagascar, from the Catalogue of the Vascular Plants of Madagascar project. Contact: Peter Phillipson - peter.phillipson@mobot.org and Marina Rabarimanarivo - marina.rabarimanarivo@mobot.mg</li> <li>ex_situ_plants.csv -- Comma-separated table with numbers of ex situ conserved plant species, per family, from BGCI’s PlantSearch database and collections of Jardin Botanique Educatif and Parc Ivoloina. Contact: Malin Rivers - malin.rivers@bgci.org</li> <li>ex_situ_vertebrates.csv -- Excel spreadsheet with the list of extant native Malagasy vertebrates with information on their presence in at least one international zoo holding and whether they have been bred successfully over the last 12 months. Data from the Zoological Information Management (ZIM) Software performed in February 2021. Contact: Angelica Crottini - tiliquait@yahoo.it</li> <li>extinct_animals_madagascar.csv -- Comma-separated table of all known anthropogenic extinctions before 1500 CE in Madagascar. Contact: Ferran Sayol - fsayol@gmail.com</li> <li>madagascar_protected_areas_sources.csv -- Comma-separated table with comments and sources for columns in the protected area data in the csv and shapefile. Contact: Maria S. Vorontsova - m.vorontsova@kew.org</li> <li>madagascar_terrestrial_protected_areas.csv -- Comma-separated values with description of the pretected areas, matching the Protected Area Shapefile. This file contains French accents; correct display may depend on the software used. Contact: Daniel Edler - daniel.edler@umu.se and Henintsoa Razanajatovo - H.Razanajatovo@kew.org</li> <li>madagascar_terrestrial_protected_areas.zip -- ESRI Shapefile for the synthesized protected areas of Madagascar, including Key Biodiversity Areas and attributes. Contact: Daniel Edler - daniel.edler@umu.se and Rasolohery Andriambolantsoa - arasolohery@ileiry.com</li> <li>observed_and_predicted_threats.csv -- Comma-separated table with the number of species with each listed threat, as defined by the IUCN or predicted by our model, across taxonomic groups. Contact: Rob Cooke - 03rcooke@gmail.com</li> <li>phylogenetic_diversity_methods.zip -- Zipped archive containing community matrices, species range shapefiles, and R script used to estimate phylogenetic diversity for amphibians, mammals, and reptiles. Contact: Weston Testo - westontesto@gmail.com</li> <li>predicting_species_IUCN_status.zip -- Zipped archive containing data, scripts and an Rstudio project to: (1) prepare features for using IUCNN v1.0 to predict the conservation status for Not Evaluated species (01_feature_preparation); (2) predict species IUCN status assessment using neural networks (02_predicting_species_IUCN_status); (3) predict species’ threat status using neural networks (03_predicting_species_threats). Contact: Alexander Zizka - alexander.zizka@biologie.uni-marburg.de and Daniele Silvestro - daniele.silvestro@unifr.ch</li> <li>threat_predictions_iucnn.txt -- Tab-separated table with results of the conservation status prediction from a Bayesian Neural Network for 5,887 species of vascular plants from Madagascar. Values are the mean posterior probabilities for each IUCN Red List category. Contact: Daniele Silvestro - daniele.silvestro@unifr.ch</li> </ul>
Data Repository: 2022 Hawai'i Cesspool Hazard Assessment & Prioritization Tool
<p>Data Repository, Codebase, inputs and Results for the Hawaii Cesspool Prioritization Tool. A project conducted by University of Hawaii Sea Grant and Water Resources Research Center, Data updated October 2022. </p> <p>Please see also: <br> https://github.com/cshuler/Act132_Cesspool_Prioritization</p> <p>and </p> <p>https://health.hawaii.gov/wastewater/files/2022/11/prioritizationtoolreport.pdf</p> <p> </p> <p> </p>
Research data repository survey data (European Research Data Landscape study)
<p>Anonymised data of the research data repository survey for the European Research Data Landscape study.</p>
Data Repository for MYRiAD: A Multi-Array Room Acoustic Database
<p>In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1s and 0.5s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. For a detailed description, please refer to the paper (<a href="https://arxiv.org/abs/2301.13057">preprint</a>, <a href="https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-023-00284-9">published</a>).</p> <p>Two files are provided, containing two different versions of the database:</p> <table> <tbody> <tr> <td><strong>MYRiAD_V2.zip</strong></td> <td>The full version of the database (31.3 GB).</td> </tr> <tr> <td><strong>MYRiAD_V2</strong><strong>_econ</strong><strong>.zip </strong></td> <td>The economy-sized version, containing source signals and RIRs only (201.7 MB).</td> </tr> </tbody> </table> <p>If you use the database, please cite the paper as follows:</p> <p>@article{dietzen2023myriad,<br> author = {Dietzen, T. and Ali, R. and Taseska, M. and van Waterschoot, T.},<br> title = {{MYRiAD}: A Multi-Array Room Acoustic Database},<br> journal = {EURASIP J. Audio Speech Music Process.},<br> volume = {2023, article no. 17},<br> number = {},<br> month = {Apr.},<br> year = {2023},<br> pages = {1--14}<br> }</p> <p>___________________________________________________________________________________________________________</p> <p>Change log (as compared to Version 1.0):</p> <ol> <li>Fixed erroneous file names in /audio/AIL/SU1/P2/.</li> <li>In the full version, applied a time shift to some of the speech, noise, and music recordings in the SAL (at most 2 samples, compensating for a slow phase drift, see manuscript for further details).</li> <li>Created an economy-sized version of the database containing source signals and RIRs only.</li> <li>Adjusted the following scripts for the economy-sized version: <br> - /tools/MATLAB/load_audio_data.m<br> - /tools/Python/load_audio_data.py</li> </ol>
The National Archives Accessions to Repositories Data c.2007 - 2020
<p>The Annual Accessions to Repositories survey is a UK-wide exercise conducted by the National Archives that assesses what is being collected by UK repositories. The primary purpose of this exercise is to place some of this information onto TNA’s search engine Discovery. More recently, the data has been used to communicate accessions trends to the wider archives sector including information on what is being collected and where. Each year, TNA sends out survey templates in the form of Excel spreadsheets that are sent out to repositories in each part of the UK. The returns sent to TNA include information on the size of the record, the dates it covers, the creator of the record and a description of the record. Work has been undertaken since October 2021 to to merge and standardise the accessions data held by TNA. This data repository presents the merged dataset.</p>
Seaweed In Nuclear Winter Data Repository
<p>The Seaweed In Nuclear Winter Data Repository contains all the necessary environmental data to run the Seaweed Growth Model and simulate the potential growth of seaweed in the aftermath of a nuclear war. This data is derived from ocean simulations for a nuclear winter scenario and includes a control run and simulations for different levels of soot emissions into the atmosphere, ranging from 5 to 150 Tg. The data in this repository can be used as input for the Seaweed Growth Model, which is available in a separate repository (<a href="https://github.com/allfed/Seaweed-Growth-Model">https://github.com/allfed/Seaweed-Growth-Model</a>). The model simulates the growth of seaweed in a nuclear winter scenario. Instructions on how to run it can be found in the code repository.</p>
WorldCereal open global harmonized reference data repository (CC-BY-SA licensed data sets)
<p>Within the<strong> ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY-SA license or a license similar to CC-BY-SA. See document "_In-situ-data-World-Cereal - license - CC-BY-SA.pdf" for an overview of the original data sets.</p>
WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)
<p>Within the <strong>ESA funded </strong>WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets. </p>
Data repository - The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios
<p>This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"</p>
Quantum critical dynamics in a 5000-qubit programmable spin glass: data repository
<p>Supporting data for "Quantum critical dynamics in a 5000-qubit programmable spin glass", Nature, 2023.</p>
Monteux et al., JGR Planets, 2023. Data Repository
<p>Source material to obtain the figures from the article entitled : Conditions for segregation of a crystal-rich layer within a convective magma ocean (JGR Planets 2023).</p> <p>The data that support the findings of this study were obtained using the commercial software COMSOL Multiphysics (version 5.4). COMSOL Multiphysics® is a simulation platform that provides fully coupled multiphysics and single-physics modelling capabilities. COMSOL Multiphysics (version 5.4) has been previously validated for two phase flow applications (Qaddah et al, 2019, Qaddah et al., 2020). To compute our simulations we used the Heat Transfer (www.comsol.com/heat-transfer-module) and Computational Flow Dynamics (www.comsol.com/cfd-module) modules in addition to the main Multiphysics platform (www.comsol.com/comsol-multiphysics). All the parameters used in our simulation are listed and described in the manuscript. The open source software used for data visualisation was Xmgrace (https://plasma-gate.weizmann.ac.il/Grace/). </p> <p>The software used for this study is the commercial software COMSOL Multiphysics (version 5.4) previously validated for two phase flow applications (Qaddah et al., 2019, Qaddah et al., 2020). User manual can be downloaded here:<br> https://doc.comsol.com/5.4/doc/com.comsol.help.cfd/CFDModuleUsersGuide.pdf. <br> A trial version of COMSOL Multiphysics may be requested (see www.comsol.com).<br> <br> More details can be found on the following COMSOL webpages:<br> - On the Foundations of the General Heat Transfer Equation:<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.heat/heat_ug_theory.07.002.html</p> <p>- Theory for Heat Transfer in Fluids :<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.heat/heat_ug_theory.07.008.html</p> <p>- The Euler–Euler Model Equations and in particular how incompressibility is handled:<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.cfd/cfd_ug_fluidflow_multi.09.153.html</p> <p>- The Boussinesq Approximation:<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.cfd/cfd_ug_fluidflow_noniso.07.20.html</p> <p> </p>
Data repository for manuscript "Contacting individual graphene nanoribbons using carbon nanotube electrodes"
<p>This is the raw data for the manuscript "Contacting individual graphene nanoribbons using carbon nanotube electrodes”.</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.