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15,702 results for “history”
Aligned DNA sequence matrix for phylogenetic analyses in the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the eastern Andean slopes and the biogeographic history of the genus"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the Amazon foothills and the biogeographic history of the genus"</p> <p>The matrix is in NEXUS format and has 3259 bp and 25 terminals.</p> <p>Partitions are as follows:</p> <div>charset 12S = 1-955;</div> <div>charset ND1_nonCoding1 = 956-1279;</div> <div>charset ND1_Pos1 = 1280-2240\3;</div> <div>charset ND1_Pos2 = 1281-2241\3;</div> <div>charset ND1_Pos3 = 1282-2242\3;</div> <div>charset ND1_nonCoding2 = 2243-2361;</div> <div>charset cmyc_Pos1 = 2362-2779\3;</div> <div>charset cmyc_Pos2 = 2363-2780\3;</div> <div>charset cmyc_Pos3 = 2364-2781\3;</div> <div>charset Rag1_Pos1 = 2782-3415\3;</div> <div>charset Rag1_Pos2 = 2783-3416\3;</div> <div>charset Rag1_Pos3 = 2784-3417\3;</div>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
Figure 9 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>This if the figure 9 of paper with DOI 10.5894/rgci-n432.</p> <p>Dunes of Trafaria and Costa da Caparica. This figure was adapted by Dissanayake M. Ruwan Sampath.</p> <p>The original source can be found at Archive from Instituto para a Conservação da Natureza e Florestas (Portugal).</p> <p>Representation of the works made by the Forestry Services between 1884 and 1910. Reference to an area flooded by the ocean in 1905 [sementeira de 1905 inundada pelo mar] and areas where new sowings had to be done [resementeiras]. Notice the drainage systems [valla] and the fences [sébe] near the coastline to protect the plants.</p>
Figure 11 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>This is the figure 11 of the article with DOI 10.5894/rgci-n432.</p> <p>Forests of Trafaria and Costa da Caparica in the 1930s-1940s. This figure was adapted by Dissanayake M. Ruwan Sampath.</p> <p>Original source can be found at the Archive of Instituto para a Conservação da Natureza e Florestas.</p> <p>In green, the existing forests. In pink, the Forestry Services areas given to other institutions or services for public uses. In yellow, the dunes to be afforested.</p> <p> </p>
Modeling Early Life Histories of Marine Organisms
<p>This is a recorded presentation to introduce students to ecosystem modeling. The presentation was developed for students of an early life histories class so discusses lagrangian individual-based modeling but the supporting material for understanding eulerian physical and lower trophic level models is also introduced.</p> <p>If you use part or all of this educational material as part of your lesson content it would be appreciated if you could inform the author (gagibson@alaska.edu) for tracking purposes.</p>
SeaLiT Knowledge Graphs - Maritime History Data in RDF using a CIDOC-CRM extension (SeaLiT Ontology)
<p><strong>SeaLiT Knowledge Graphs</strong> is an RDF dataset of maritime history data that has been transcribed (and then transformed) from original archival sources in the context of the <a href="http://www.sealitproject.eu/">SeaLiT Project</a> (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). The underlying data model is the <a href="https://zenodo.org/record/5964240">SeaLiT Ontology</a>, an extension of the ISO standard <strong>CIDOC-CRM</strong> (ISO 21127:2014) for the modelling and integration of maritime history information. </p> <p>The knowledge graphs integrate data of totally 16 different types of archival sources:</p> <ul> <li>Crew Lists <ul> <li>Crew and displacement list (Roll)</li> <li>Crew List (Ruoli di Equipaggio)</li> <li>General Spanish Crew List</li> </ul> </li> <li>Registers / Lists <ul> <li>Students Register</li> <li>Civil Register</li> <li>Register of Maritime Personnel</li> <li>Register of Maritime Workers (Matricole della gente di mare)</li> <li>Sailors Register (Libro de registro de marineros)</li> <li>Naval Ship Register List</li> <li>Seagoing Personnel</li> <li>Lists of ships</li> </ul> </li> <li>Censuses <ul> <li>Census La Ciotat</li> <li>First National all-Russian Census of the Russian Empire</li> </ul> </li> <li>Payrolls <ul> <li>Payrolls of private archives and libraries in Greece</li> <li>Payrolls of Russian Steam Navigation and Trading Company</li> </ul> </li> <li>Employment records <ul> <li>Shipyards of Messageries Maritimes, La Ciotat</li> </ul> </li> </ul> <p>More information about the archival sources are available through the <a href="https://sealitproject.eu/dictionary-of-source-types-list">SeaLiT website</a>. Data exploration applications over these sources are also publicly available (<a href="https://catalogues.sealitproject.eu/">SeaLiT Catalogues</a>, <a href="http://rs.sealitproject.eu/">SeaLiT ResearchSpace</a>). </p> <p>Data from these archival sources has been transcribed in tabular form and then curated by historians of SeaLiT using the <a href="https://www.ics.forth.gr/isl/fast-cat">FAST CAT</a> system. The transcripts (records), together with the curated vocabulary terms and entity instances (ships, persons, locations, organizations), are then transformed to RDF using the SeaLiT Ontology as the target (domain) model. To this end, the corresponding schema mappings between the original schemata and the ontology were defined using the <a href="https://github.com/isl/x3ml">X3ML</a> mapping definition language, that were subsequently used for delivering the RDF datasets. </p> <p>More information about the FAST CAT system and the data transcription, curation and transformation processes can be found in the following paper:</p> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>]</p> </blockquote> <p>The RDF dataset is provided as a set of TriG files per record per archival source. For each record, the dataset provides: i) one trig file for the record's data (<em>records.trig</em>), ii) one trig file for the record's (curated) vocabulary terms (<em>vocabularies.trig</em>), and iii) four trig files for the record's (curated) entity instances (<em>ships.trig, persons.trig, persons.trig, organizations.trig</em>).</p> <p>We also provide the RDFS files of the used ontologies (SeaLiT Ontology verson 1.0, CIDOC-CRM version 7.1.1). </p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.</p>
History in mathematics education - 6th grade
<p>Data set related to an experiment on the use of history of mathematics (ancient Chinese numeration) carried out with 108 sixth grade students. The experiment consists of three parts: ordinary mathematical exercises (items Mxx), an activity (in class, without data), and an evaluation (items Hxx).</p> <p>The experiment was carried out in October 2021.</p>
Data for 'Phenological shifts in a warming world affect physiology and life history in a damselfly'
<p>In this analysis we studied, in laboratory conditions, the impact of warming and hatching dates on key life history and physiological traits in a cannibalistic damselfly, <em>Ischnura</em> <em>elegans</em>. Larvae were reared in groups from hatching to emergence through one or two growth seasons, depending on the voltinism. Larvae were equally divided by hatching dates (early and late) and temperature treatment (current and warming). Early and late hatched groups were not mixed. This data set includes:</p> <ul> <li>survival until emergence and emergence success data</li> <li>development time (from hatching to emergence, in days)</li> <li>mass of adult insects</li> <li>growth rate (mass of adult insect/development time in days)</li> <li>protein content (μg of protein/μl of prepared homogenate)</li> <li>phenoloxidase activity (PO, value of activity curve slope)</li> <li>PO activity/protein (value of activity curve slope/(μg of protein/μl of prepared homogenate)</li> <li>insects voltinism (univoltine/semivoltine)</li> </ul>
Biogeographic history of a large clade of ectomycorrhizal fungi, the Russulaceae, in the Neotropics and adjacent regions
<p>## Metadata</p> <p>backbone_accessions.tsv - GenBank/INSDC accession numbers for LSU, rpb1 and rpb2 accessions used for the Russulaceae backbone tree including 472 taxa.</p> <p>ITS_sequences_OTUs.tsv - Metadata for all 34,624 ITS sequences used in the study. Columns: "accession": accession ID in analysis – GenBank/INSDC or UNITE accession number for compiled data, lab ID for newly generated data; "specimen": specimen/voucher number, for newly generated sequences; "INSDC_accession": INSDC/GenBank accession for new newly generated data; "taxon": specimen identification; "New": whether ITS sequences was generated in this study (*); "OTU": name of cluster/OTU, if not the sequence accession itself (*); "In_tree": whether sequence is represented in the Russulaceae supertree after filtering steps (*), "lb" long-branch accession removed during tree estimation, "ol" outlier removed during tree estimation; "area": biogeographic area assigned.</p> <p> </p> <p>## Sequences and alignments</p> <p>backbone_concat.fasta - Concatenated LSU-rpb1-rpb2 alignment for 372 backbone taxa.</p> <p>backbone_concat_part.txt - Gene partitions and substitution models applied to the backbone alignment.</p> <p>einsi_clade1_Russula_trimmed.fasta - Alignment of 2,279 representative ITS sequences in the Russula clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade2_LactariusMultifurca_trimmed.fasta - Alignment of 621 representative ITS sequences in the Lactarius-Multifurca clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade3_Lactifluus_trimmed.fasta - Alignment of 482 representative ITS sequences in the Lactifluus clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p> </p> <p>## Phylogenetic trees</p> <p>12_make_supertree.R - R script for grafting clade trees onto the backbone tree to produce a supertree.</p> <p>backbone_calibrated.nwk - Time-calibrated Russulaceae backbone phylogeny.</p> <p>backbone_TBE.raxml.support - Russulaceae backbone phylogeny annotated with transfer bootstrap expectation support values.</p> <p>clade1_Russula_TBE.raxml.support - Russula subclade ITS phylogeny (2,279 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade2_LactariusMultifurca_TBE.raxml.support - Lactarius-Multifurca subclade ITS phylogeny (621 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade3_Lactifluus_TBE.raxml.support - Lactifluus subclade ITS phylogeny (482 tips), annotated with transfer bootstrap expectation support values.</p> <p>supertree_calibrated.nwk - Combined Russulaceae supertree, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade1_Russula.nwk - Russula subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade2_LactariusMultifurca.nwk - Lactarius-Multifurca subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade3_Lactifluus.nwk - Lactifluus subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p> </p> <p>## Biogeographic analysis</p> <p>3_disp_counts.R - R script to count dispersal events between biogeographic areas, based on stochastic mapping output.</p> <p>9_disp_count_time.R - R script to count dispersal events to and from each area through time, based on stochastic mapping output.</p> <p>area_codes.tab - Area letter coding and colours used for biogeographic analysis and plotting.</p> <p>area_shapes.zip - Shapefiles for the nine biogeographic areas defined, based on merged areas from Dinerstein et al. 2017 (https://doi.org/10.1093/biosci/bix014) and Löwenberg-Neto (2014: https://doi.org/10.11646/zootaxa.3802.2.12; 2015: https://doi.org/10.11646/10.11646/zootaxa.3985.4.9).</p> <p>areas_manually_zenodo.csv - Manual assignment of 800 ITS sequences to biogeographic areas based on associated literature records or metadata.</p> <p>corHMM_ER.Rdata - R data archive with input data and results for the corHMM/Mv biogeographic area reconstruction.<br> <br> corHMM_ER_stoch_maps.Rdata - R data archive with results from the corHMM/Mv biogeographic stochastic mapping.</p> <p>disp_counts_focal.tab - Dispersal counts to and from each focal area through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_counts_sam_afr.tab - Dispersal counts between Afrotopics and lowland tropical S. America through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_matrix_025.txt - Dispersal rates between biogeographic areas (2.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_975.txt - Dispersal rates between biogeographic areas (97.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_median.txt - Dispersal rates between biogeographic areas (median values), based on stochastic mapping output.</p> <p> </p> <p>## Diversification analysis</p> <p>5_rates_per_area.R - R script to partition diversification rates by biogeographic area, both overall and through time, based on BAMM diversification rates and area stochastic mapping.</p> <p>event_data.txt - Posterior samples of diversification rate regimes estimated with BAMM.</p> <p>div_rates_area_overall.txt - Overall diversification rates per biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_025.tsv - Diversification rates through time (2.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_975.tsv - Diversification rates through time (97.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_median.tsv - Diversification rates through time (means) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>mcmc_out.txt - BAMM posterior sample characteristics.</p>
The Natural History Museum's collection of Dalbergia, Pterocarpus and the Phaseolinae subtribe
<p>In 2018 the Natural History Museum (NHMUK, herbarium code: BM) undertook a pilot digitisation project together with the Royal Botanic Gardens Kew (project Lead) and the Royal Botanic Garden Edinburgh to collectively digitise non-type herbarium material of the subtribe <em>Phaseolinae</em> and the genera <em>Dalbergia </em>L.f. and <em>Pterocarpus</em> Jacq. (rosewoods and padauk), all from the economically important family of legumes (<em>Leguminosae</em> or <em>Fabaceae</em>). </p> <p>These taxonomic groups were chosen for two case studies using the herbarium collections to support the aims of the UK’s Department for Environment Food & Rural Affairs (DEFRA)-allocated, Official Development Assistance (ODA) funding: study 1 - to support the development of dry beans as a sustainable and resilient crop; study 2 - to aid conservation and sustainable use of rosewoods and padauk.</p> <p>We present the images and metadata for 11,222 NHMUK specimens. This includes label transcription and georeferencing, along with summary data on geographic, taxonomic, collector and temporal coverage. We also provide timings and the methodology for our transcription and georeferencing protocols. Approximately 35% of specimens digitised were collected in ODA-listed countries, in tropical Africa, but also in south east Asia and South America.</p>
History_09/29/22
Documentation material from the Mastic pilot of the Mingei project
Stressful crystal histories recorded around melt inclusions in volcanic quartz
<p>Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals preserve similar and relatively small magnitudes of elastic residual stress (mean 53-135 MPa, median 46-116 MPa) in comparison to the strength of quartz (~10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption. The data collected using Laue microdiffraction at Lawrence Berkeley National Laboratory Advanced Light Source beamline 12.3.2 are included below as .xlsx files. Data was processed and analyzed using XMAS (Tamura, 2014) and XtalCAMP (Li et al., 2020).</p>
OSHDB - OpenStreetMap History Data Analysis
<p>A high-performance framework for spatio-temporal data analysis of OpenStreetMap full-history data. Developed by <a href="https://heigit.org">HeiGIT</a> as part of the <a href="https://ohsome.org/">ohsome project</a>.</p> <p><em>Code is hosted on github: <a href="https://github.com/giscience/oshdb">https://github.com/giscience/oshdb</a>.</em></p> <p>The OSHDB allows to investigate the evolution of the amount of data and the contributions to the OpenStreetMap project. It combines easy access to the historical OSM data with high querying performance. Use cases of the OSHDB include data quality analysis, computing of aggregated data statistics and OSM data extraction. The main functionality of the OSHDB is explained in the <a href="https://github.com/GIScience/oshdb/blob/master/documentation/first-steps/README.md">first steps tutorial</a>.</p> <p><strong>OpenStreetMap History Data</strong></p> <p><a href="https://www.openstreetmap.org/">OpenStreetMap</a> contains a large variety of geographic data, differing widely in scale and feature type. OSM contains everything from single points of interests to whole country borders, from concrete things like buildings up to more abstract concepts such as turn restrictions. OSM also offers metadata about the <a href="https://wiki.openstreetmap.org/wiki/Planet.osm/full">history</a> and the modifications that are made to the data, which can be analyzed in a multitude of ways.</p> <p>Because of it's size and variety, possibilities of working with OSM history data are limited and there exists a lack of an easy-to-use analysis software. A goal of the OSHDB is to make OSM data more accessible to researchers, data journalists, community members and other interested people.</p> <p><strong>Central Concepts</strong></p> <p>The OSHDB is designed to be appropriate for a large spectrum of potential use cases and is therefore built around the following central ideas and design goals:</p> <ul> <li><em>Lossless Information</em>: The full OSM history data set should be stored and be queryable by the OSHDB, including errorneous or partially incomplete data.</li> <li><em>Simple, Generic API</em>: Writing queries with the OSHDB should be simple and intuitive, while at the same time flexbile and generic to allow a wide variety of analysis queries.</li> <li><em>High Performance</em>: The OSM history data set is large and thus requires efficiency in the way the data is stored and in the way it can be accessed and processed.</li> <li><em>Local and Distributed Deployment</em>: Analysis queries should scale well from data explorations of small regions up to global studies of the complete OSM data set.</li> </ul> <p>The OSHDB splits data storage and computations. It is then possible to use the <a href="https://en.wikipedia.org/wiki/MapReduce">MapReduce</a> programming model to analyse the data in parallel and optionally also on distributed databases. A central idea behind this concept is to bring the code to the data.</p>
Science ready spectra of star clusters and their best-fitting models described in the research paper "Using Star Clusters as Tracers of Star Formation and Chemical Evolution: the Chemical Enrichment History of the Large Magellanic Cloud" by Chilingarian & Asa'd
<p>Science ready spectra of star clusters in the Large Magellanic Cloud and their best-fitting templates (alpha-enhanced MILES based simple stellar population models) obtained using the NBursts full spectrum fitting code. Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For each cluster, 5 spectra are provided, which correspond to [alpha/Fe] values from 0.0 to 0.4 dex with a step of 0.1 dex. The only exception is NGC2249, for which only 3 models are provided. The alpha-enhancement value of a model grid used in the fitting procedure is given in the FITS keyword MGFEGRID.</p>
Herbarium specimen image of Edwardsia grandiflora Salisb., part of the collection of Natural History Museum London
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Hypericum majus Britton, part of the collection of Natural History Museum London
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Hypericum majus Britton, part of the collection of Natural History Museum London
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Hypericum ellipticum Hook., part of the collection of Natural History Museum London
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Hypericum ellipticum Hook., part of the collection of Natural History Museum London
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
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.