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3,118 results for “resources”
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
A curated data resource of 214K metagenomes for characterization of the global resistome
<p><strong>Data files of the curated resource of 214K metagenomes </strong> <strong>for characterization of the global resistome.</strong></p> <p>We have retrieved 214K metagenomic samples and now share the results here on Zenodo of our large-scale read mapping effort.</p> <p>There are five tables uploaded in three formats (TSV, HDF and MySQL dump):</p> <ul> <li>metadata.* : contains metadata for all sequencing runs.</li> <li>ARG.* : contain read alignment counts of antimicrobial resistance genes (ARGs).</li> <li>rRNA.* : contain read alignment counts of 16S/18S rRNA genes.<sup>1</sup></li> <li>diversity.* : contain diversity measures for ARGs and two taxonomic groups of rRNA genes (phylum, genus).</li> <li>ResFinder_anno.* : contain sequence information on the different ARGs, such as gene_lengths, resistance class, etc.</li> </ul> <p>Note that the HDF file rRNA.h5 is split into batches of 10,000 rows. To load it, the keys are in the format of "table_{i}", where i=0,1,2,..,4736</p> <p>Details on the different tables are available at https://hmmartiny.github.io/mARG/</p> <p>Additionaly, we have shared the data used to create the figures in the manuscript in the ZIP file named "figure_data.zip".</p> <p>Any further questions or issues, please contact H.-M. Martiny at hanmar@food.dtu.dk</p> <p> </p> <p><strong>Update log</strong>:</p> <p>* 2023-01-20: Update Diversity tables due to wrong total_fragments entered for ~250 run_accessions.</p>
Umfragedaten: Studentische Perspektiven auf Open Educational Resources in der Rechtswissenschaft
<p>Der Datensatz enthält die Rohdaten, ausgewerteten Daten und das dazugehörige R-Skript für eine 2022 durchgeführte Umfrage unter Studierenden der Rechtswissenschaft in Deutschland zu Chancen und Grenzen von Open Educational Resources. Für weitere Fragen zu der Umfrage wenden Sie sich bitte an <a href="https://www.jura.uni-muenster.de/de/institute/lehrstuhl-fuer-oeffentliches-recht-voelker-und-europarecht-sowie-empirische-rechtsforschung/team/weitere-personen/max-milas/">Max Milas</a> oder <a href="https://www.jura.fu-berlin.de/fachbereich/einrichtungen/oeffentliches-recht/lehrende/calliessc/Mitarbeiterinnen-und-Mitarbeiter/Wissenschaftliche_Mitarbeiter_innen/Valentina-Chiofalo/index.html">Valentina Chiofalo</a>. </p>
The URW-KG: a Resource for Tackling the Under-Representation of non-Western Writers
<p>Digital media have enabled the access to an unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.<br> </p>
Maternal fetal ultrasound planes from low-resource imaging settings in five African countries
<p>This resource is a dataset of routinely acquired maternal-fetal screening ultrasound images collected in five centers of five countries in Africa (Malawi, Egypt, Uganda, Ghana and Algeria) that is associated to the journal article Sendra-Bacells et al. "Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries", <em>Scientific Reports</em>. The images correspond to the four most common fetal planes: abdomen, brain, femur and thorax. A CSV file is provided where image filenames are associated to plane types and patient number as well as the partitioning in training and testing splits as used in the associated publication.</p>
Mitochondrial genome sequencing and analysis of the invasive Microstegium vimineum: a resource for systematics, invasion history, and management
<p>Table S1: Accession data for Microstegium samples included in this study.</p> <p>File S1: Alignment of Mitochondrial CDS for Poales mitochondrial sequences.</p> <p>File S2: SNP data for Microstegium vimineum mitochondrial variants.</p> <p>Figure S1: Transposable element content in the Microstegium vimineum mitogenome.</p> <p>Figure S2: Summary of Kraken2 output.</p> <p> </p>
CrusTome: A transcriptome database resource for large-scale analyses across Crustacea
<p>CrusTome_v0.1.0 Prerelease<br> /ReadMe - this file<br> /crustome_aa_BLAST.tar.gz - CrusTome database of amino acid sequences in BLAST format<br> /crustome_aa_DIAMOND.tar.gz - CrusTome database of amino acid sequences in DIAMOND format<br> /crustome_mrna_BLAST.tar.gz - CrusTome database of mRNA sequences in BLAST format<br> /dict - Dictionary file to translate species IDs. For usage with sed/awk see link to Github site below<br> <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> * <br> * Please note, most of the data files contained in this DOI are <br> * compressed into GZip files (.gz extension). <br> * Mac and Linux OS's can extract this file type natively. <br> * Windows OS requires software to extract the archive. 7-Zip <br> * (http://www.7-zip.org) is free and open source software that will <br> * allow windows PCs to open and decompress the archive. <br> * <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> <br> <strong><em>Pérez-Moreno JL, Kozma MT, DeLeo DM, Bracken-Grissom HD, Durica DS, Mykles DL. 2023. CrusTome: A transcriptome database resource for large-scale analyses across Crustacea. G3: Genes, Genomes, Genetics.</em></strong></p> <p><strong>CrusTome: A transcriptome database resource for large-scale analyses across Crustacea</strong></p> <p>Transcriptomes from non-traditional model organisms often harbor a wealth of unexplored data. Examining these datasets can lead <br> to clarity and novel insights in traditional systems, as well as to discoveries across a multitude of fields. Despite significant <br> advances in DNA sequencing technologies and in their adoption, access to genomic and transcriptomic resources for non-traditional <br> model organisms remains limited. Crustaceans, for example, being amongst the most numerous, diverse, and widely distributed taxa on the planet, often serve as excellent systems to address ecological, evolutionary, and organismal questions. While they are <br> ubiquitously present across environments, and of economic and food security importance, they remain severely underrepresented in <br> publicly available sequence databases. Here, we present CrusTome, a multi-species, multi-tissue, transcriptome database of 201 <br> assembled mRNA transcriptomes (189 crustaceans, 30 of which were previously unpublished, and 12 ecdysozoan outgroups) as an evolving, and publicly available resource. This database is suitable for evolutionary, ecological, and functional studies that employ<br> genomic/transcriptomic techniques and datasets. CrusTome is presented in BLAST and DIAMOND formats, providing robust datasets for sequence similarity searches, orthology assignments, phylogenetic inference, etc., and thus allowing for straight-forward incorporation into existing custom pipelines for high-throughput analyses.</p> <p> <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> <br> For questions regarding released datasets contact:<br> Corresponding Author: Jorge L. Perez-Moreno (Colorado State University)<br> jorgepm@colostate.edu / jpere645@fiu.edu<br> <br> <strong> https://github.com/invertome/crustome</strong></p> <p> </p> <p> <br> <strong>PLEASE CITE:</strong></p> <p>Pérez-Moreno JL, Kozma MT, DeLeo DM, Bracken-Grissom HD, Durica DS, Mykles DL. 2023. CrusTome: A transcriptome database resource for large-scale analyses across Crustacea. G3: Genes, Genomes, Genetics.</p> <p> </p> <p><strong>Funder Information</strong></p> <p>Supported by National Science Foundation grants to DLM (IOS-1922701) and DSD (IOS-1922755). In addition, this work was partially funded by two grants awarded from the National Science Foundation: Doctoral Dissertation Improvement Grant (#1701835) awarded to JPM and HBG and the Division of Environmental Biology Bioluminescence and Vision grant (DEB-1556059) awarded to HBG. Samples in the FICC were collected by grants from The Gulf of Mexico Research Initiative (GOMRI), Florida Institute of Oceanography Shiptime Funding awarded to HBG and DMD; the National Science Foundation Division of Environmental Biology Grant 1556059 awarded to HBG; and the National Oceanic and Atmospheric Administration Ocean Exploration Research (NOAA-OER 2015) grant awarded to HBG.</p>
Raw data from: "Thermal mismatches explain consumer-resource dynamics in response to environmental warming".
<p>Data from:</p> <p>Álvarez-Codesal, S, Faillace CA, Garreau, A, Bestion, E, Synodinos, AD, & Montoya, JM. 2023. Thermal mismatches explain consumer-resource dynamics in response to environmental warming. Ecology and Evolution.<br> </p> <p>Composed of seven associated datasets:</p> <p>- Algae_Net_Photosynthesis.csv</p> <p>- Daphnia_Ingestion.csv</p> <p>- Algae_Respiration.csv</p> <p>- Daphnia_Ingestion.csv</p> <p>- Daphnia_respiration.csv</p> <p>- Interaction_Strength.csv</p> <p>- Energy_Balance_Ratios_dataset.csv</p> <p> </p> <p>A detailed explanation of the methods is provided in the related Alvarez-Codesal et al. 2023 article, please refer to it for a full understanding of the methods and results.</p> <p>Briefly: our aim was to quantify the impact of warming on consumer-resource interactions, using as consumer <em>Daphnia pulex</em>, and as resources two algae species, <em>Chlamydomonas reinhardtii</em> and <em>Desmodesmus</em> sp.</p> <p>First, we measured thermal dependencies of physiological rates related to energy gain (i.e., ingestion, net photosynthesis) and loss (i.e., respiration) for <em>Daphnia</em> and the algae in basal conditions at eight temperatures. From these, we calculated species energetic balances as net photosynthesis-to-respiration ratio (P/R<sub>r</sub>) for resources, or as ingestion-to-respiration ratio (I/R<sub>c</sub>) for consumers, using predicted values of each (per capita) rate from the models. We used energetic balances as an indicator of how species respond to increasing temperatures, defining intraspecific energetic mismatches when the energetic balance decreases.</p> <p>Second, we inferred interspecific thermal mismatches (inter-TM) by comparing individual energetic balances for both consumer-resource pairs, and identified the thermal mismatch regions, where: 1) trends of energetic balances contrast between the interacting species; and 2) both species reduce their energetic balance with increasing temperature. The inter-TMs were used to increase our qualitative understanding on the outcomes of thermal dependencies of interaction strength.</p> <p>Third, we calculated the natural logarithm of the consumer-resource energetic balance for each interacting pair as the ratio between consumer energetic balance and the resource energetic balance to get qualitative predictions on the trends of interaction strength with temperature.</p> <p>Finally, we measured experimentally thermal dependencies of interaction strength for each consumer-resource pair and verified if our predictions using the thermal mismatches fitted the trends of interaction strengths across temperatures.</p>
Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"
<p>Supplementary datasets accompanying the manuscript "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds" published in the Metabolic Engineering Journal (<a href="https://doi.org/10.1016/j.ymben.2022.03.013">https://doi.org/10.1016/j.ymben.2022.03.013). </a>In line with the standards of open science, the ARBRE toolbox is freely available to the scientific community on gitHub (<a href="https://github.com/EPFL-LCSB/ARBRE">https://github.com/EPFL-LCSB/ARBRE</a>) and we also provide the web-version at <a href="http://lcsb-databases.epfl.ch/arbre/">http://lcsb-databases.epfl.ch/arbre/</a></p> <p>ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer is a new computational resource consisting of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 33′000 known and 390′000 novel reactions predicted with generalized enzymatic reactions rules and over 74′000 compounds, of which 19′000 are known to biochemical databases and 55′000 only to PubChem. Over 1′000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included in the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways and products of lignin degradation to predict valuable compound derivations.</p> <p>Supplementary files are organized as follows:</p> <p>- 1-s2.0-S1096717622000490-mmc4.docx contains Supplementary Figures 1-4 and Tables 1, 2, and 4.</p> <p>- 1-s2.0-S1096717622000490-mmc2.xlsx contains Supplementary Table 3.</p> <p>- 1-s2.0-S1096717622000490-mmc1.xlsx contains Supplementary Table 5</p> <p>- 1-s2.0-S1096717622000490-mmc3.xlsx contains Supplementary Table 6</p> <p> </p> <p> </p> <p> </p> <p> </p>
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
Allliance of Genome Resources Orthology
<p>Tab separated formatted spreadsheet of orthology annotations from the Alliance of Genome Resources.</p> <p>The Alliance provides the results of all methods that have been benchmarked by the <a href="https://questfororthologs.org/">Quest for Orthologs Consortium (QfO)</a>, as well as curated ortholog inferences from HGNC (for human and mouse genes), Xenbase (for frog genes), and ZFIN (relating zebrafish genes to orthologs in human, mouse, and fly).</p> <p>The ortholog inferences from the different methods have been integrated using the DRSC Integrative Ortholog Prediction Tool (DIOPT). DIOPT integrates a number of existing methods including those used by the Alliance: Ensembl Compara, HGNC, Hieranoid, InParanoid, OMA, OrthoFinder, OrthoInspector, PANTHER, PhylomeDB, SonicParanoid, Xenbase, and ZFIN. See the <a href="https://fgr.hms.harvard.edu/diopt-documentation">DIOPT documentation</a> for additional information and references related to the included methods. DIOPT assigns a score/count based on the number of methods that call a specific ortholog. For noncoding RNA genes, currently only HGNC and ZFIN curated orthologs are included.</p> <p>File includes orthology relationships among genes from the following organisms:</p> <ul> <li>Homo sapiens (human; NCBI:txid 9606)</li> <li>Caenorhabditis elegans (nematode; NCBI:txid 6239)</li> <li>Danio rerio (zebrafish;NCBI:txid 7955)</li> <li>Drosophila melanogaster (fruit fly; NCBI:txid 7227)</li> <li>Mus musculus (mouse; NCBI:txid10090)</li> <li>Rattus norvegicus (rat; NCBI:txid 10116)</li> <li>Saccharomyces cerevisiae (yeast; NCBI:txid 559292)</li> <li>Xenopus laevis (African clawed frog; NCBI:txid 8355)</li> <li>Xenopus tropicalis (Western clawed frog; NCBI:txid 8364)</li> </ul>
Alliance of Genome Resources Genetic Interactions
<p>These files provide a set of annotations of genetic interactions for genes for human, rat, mouse, zebrafish, fruit fly, nematode, African clawed frog,and yeast). The files are in the <a href="https://github.com/HUPO-PSI/miTab/blob/master/PSI-MITAB27Format.md">PSI-MI TAB 2.7 format</a>, a tab-delimited format established by the <a href="http://www.psidev.info/">HUPO Proteomics Standards Initiative</a> Molecular Interactions (PSI-MI) working group. The interaction data are sourced from Alliance members WormBase and FlyBase, as well as the <a href="https://thebiogrid.org/">BioGRID database</a>. Identities or types of genetic perturbations for each interactor (if available) are provided in columns 26 and 27 and relevant phenotypes or traits (if available) are provided in column 28.</p> <ul> <li>Homo sapiens (human; NCBI:txid 9606)</li> <li>Caenorhabditis elegans (nematode; NCBI:txid 6239)</li> <li>Danio rerio (zebrafish;NCBI:txid 7955)</li> <li>Drosophila melanogaster (fruit fly; NCBI:txid 7227)</li> <li>Mus musculus (mouse; NCBI:txid10090)</li> <li>Rattus norvegicus (rat; NCBI:txid 10116)</li> <li>Saccharomyces cerevisiae (yeast; NCBI:txid 559292)</li> <li>Xenopus laevis (African clawed frog; NCBI:txid 8355)</li> </ul>
New Soil Metagenome-Assembled Genomes Catalogue Boosts Genetic Resources
<p><strong>Soil harbors a vast expanse of unidentified microbes, termed as microbial dark matter, presenting an untapped reservoir of microbial biodiversity and genetic resources, but has yet to be fully explored. In this study, we conducted the first large-scale excavation of soil microbial dark matter by reconstructing 40,039 metagenome-assembled genome bins (the SMAG catalog) from 3,304 soil metagenomes. We identified 16,530 of 21,077 species-level genome bins (SGBs) as unknown SGBs (uSGBs), which greatly expand archaeal and bacterial diversity across the tree of life. We also illustrate the pivotal role of uSGBs in augmenting soil microbiome's functional landscape and intra-species genome diversity, providing large proportions of the 43,169 biosynthetic gene clusters and 8,545 CRISPR-Cas genes. Additionally, we determined that uSGBs contributed 84.6% of novel viral-host associations identified from the SMAG catalog. Our results propose the SMAG catalog, a novel and expansive genomic resource that brings the soil microbial biodiversity and novel genetic resources to light.</strong></p>
Data from: Butterflies are not a robust bioindicator for assessing pollinator communities, but floral resources offer a promising way forward
<p>Monitoring pollinators is crucial for the evaluation of biodiversity and potential pollination services. Yet, efficiently monitoring multiple taxa over large areas can be costly. An alternative approach is using simple species bioindicators that represent the entire pollinator community. One of the requirements of a good bioindicator is that it can be easily identified to lower taxonomic levels and be sensitive to changes in habitat. This is the case for butterflies, a taxon for which many countries have a country-wide long-term monitoring scheme. We tested whether butterfly diversity can be used to predict diversity of bees and hoverflies both spatially and temporally. We surveyed 42 transects of the Dutch Butterfly Monitoring Scheme in 2020, to record species richness and abundance of butterflies, bees and hoverflies. We also recorded flower area and richness in the pollinator transects. To test whether pollinators with similar functional traits are more closely correlated than the entire pollinator community, we categorized bee and butterfly species according to their diet breadth (polyphagous vs. non-polyphagous), nitrogen-affinity (nitrophobous vs. nitrophilous larval resources) and body size. We used the same methods to test for temporal correlations over seven years for one site in Spain. Butterfly richness was not spatially correlated with bee richness (Pearson's r = 0.13), nor were the two taxa temporally correlated (Pearson's r = 0.02). Interestingly, hoverfly richness was spatially correlated with butterfly richness (Pearson's r = 0.43) and with bee richness (Pearson's r = 0.36) in the Netherlands and, hence, hoverflies might be slightly more suitable as a bioindicator of pollinator diversity in this area. Abundance of all three taxa showed no significant inter-correlation, except for correlations between diet specialist bees and butterflies (Pearson's r = 0.39). Importantly, all three taxa were strongly correlated with flower richness, but they varied in their preferences for host plant families. This is in line with 75% of the plant-pollinator studies finding significant positive relations. For monitoring schemes to be effective in informing better pollinator conservation, they should expand to include bees and hoverflies as well as simple indicators of habitat quality such as floral resources.</p>
The World Asellidae database and phylogeny: a collaborative backbone resource for comparative studies of subterranean life evolution
<p>Supplementary material for the article "The World Asellidae database and phylogeny: a collaborative backbone resource for comparative studies of subterranean life evolution"</p> <p>- SI Figure 5: The World Asellidae phylogeny with credibility Intervals for the age of the nodes. Node labels of the phylogeny indicate the 95% credibility intervals of the estimated dates.</p> <p>- SI Table 1: Metadata for the 2093 COI sequences used in the study.</p> <p>- SI Table 4: Alignment of the 2093 COI sequences used for the delimitation of MOTUs.</p> <p>- SI Table 5: Alignment of the 424 COI sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 6: Alignment of the 424 16S sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 7: Alignment of the 424 FASTKD4 sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 8: Alignment of the 424 28S sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 9: Metadata for the DNA sequences used for the 4-gene dated phylogeny.</p> <p>- SI Table 11: Data on body size, sexual body size dimorphism, habitat specialization and habitat size used in comparative analyses.</p> <p>- SI Table 12: Metadata for the DNA sequences deposited in NCBI as part of this study.</p>
Evaluation of buckwheat genetic resources in Slovenia within the ECOBREED project
<p>Publication and supplementary data:</p> <p>Data related to the publication in Fagopyrum 40 (2):67-76: field evaluation data (seed weight, plant height, protein content) and concentrations of phenolic compounds and antioxidant capacity of 17 buckwheat genetic resources and 6 commercial varieties tested in two years (2020 & 2021) in Slovenia.</p>
Dataset about response of some ciliate species to resources and light
<p>Communities of seven species of ciliates were grown at 10 different levels of resources and four levels of light. The two treatments were manipulated factorially. Most treatment combinations were replicated once, some twice. The dataset contains the abundances of the seven ciliate species after seven days of growth at the respective levels of resources and light.</p>
Adirondack Public Good Events Database: Natural Resource, Environmental, Economic and Recreation Policy and Common Pool Resources for a Social-Ecological System in Adirondack Park, New York, USA, 1760-2020.
I assembled this dataset from various published sources to evaluate how the social-ecological system (SES) in Adirondack Park, New York changed through time and the interplay of public goods (Common Pool Resources, CPRs), public land rules and private land rights, and related concepts over 260 years (1760-2020). The database was the basis for a doctoral dissertation titled "Blue Lining: Assessing the Resilience of Adirondack Park, New York Using Polycentricity and Panarchy Frameworks." The goal of the dissertation was to assess patterns and changes in institutional rules, actors and arrangements before and after establishment of the public Adirondack Forest Preserve in 1885 and Adirondack Park in 1892 as those actors and rules were modified and as both internal and external events influenced the SES as it moved through different phases of the adaptive cycle through space and time (see panarchy). Using the database, I identified which organizations and events contributed to natural resource and CPR policy. The dissertation can be downloaded here: https://experts.esf.edu/esploro/outputs/99917370604826.
Willamette National Forest soil resource inventory (SRI 1992) clipped to the Andrews Experimental Forest
This layer contains the delineations of the soil landtype mapping units defined in the 1992 update to the SRI (Soil Resource Inventory) The mapping units are derived and defined on the basis of soil, landform, geology and vegetation characteristics. The average delineation size for each mapping unit is 50 to 600 acres. This data was originally mapped at the 1:24000 scale. Due to the reconnaissance nature of this survey, it lacks detail for use in high-intensity, small-area projects. These projects require additional on-site study by various technical specialists, including soil scientists. The dominant landtype of the mapping unit is described in the mapping unit description and identified by the same number as used for the mapping unit. Within the mapping unit, other landtypes occur. Those most commonly associated with the dominant landtype of the mapping unit are included in the descriptions as inclusions. These inclusions of other landtypes account for no more than 30 percent of the mapping unit. (for further descriptions of mapping unit symbology see SOIL RESOURCE INVENTORY" Willamette National Forest, Basic Soil Information and Interpretive Tables
Plant community richness and foliar fungicides impact soil Streptomyces inhibition, resistance, and resource use phenotypes
Data associated with "Plant community richness and foliar fungicides impact soil Streptomyces inhibition, resistance, and resource use phenotypes" (DOI: 10.3389/fmicb.2024.1452534). These data include soil resource measurements and various phenotypic measurements of associated Streptomyces isolates/populations. Specifically, these data note population level inhibition phenotypes according to Herr's Assays, isolate level antibiotic resistance phenotypes against 9 standard antibiotics, and isolate level resource use phenotypes quantified with Biolog SF-P2 96 well plates.
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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)
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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.