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6,861 results for “animation”
Decomposition, porewater, plant and animal collection, and soil temperature data in Airport Marsh, Sapelo Island, 7/2019-7/2020
Environmental gradients can affect organic matter decay within and across wetlands and contribute to spatial heterogeneity in soil carbon stocks. We tested the sensitivity of decay rates to tidal flooding and soil depth in a minerogenic salt marsh using the tea bag index (TBI). Tea bags were buried at 10- and 50- cm along transects sited at lower, middle, and higher elevations that paralleled a headward eroding tidal creek. Plant and animal communities and soil properties were characterized once while replicate tea bags and porewaters were collected 3 and 4 times respectively over one year.
Occurrence of animals along five transects at the Jornada Basin LTER site from 1989-1994
This dataset contains data on the occurrence of rabbits, birds, and lizards observed along the Jornada Basin LTER (II) animal transects in southern New Mexico, USA. Five, 1 km transects were established, each in a different vegetation zone, near the current NPP study locations C-CALI, G-IBPE, M-NORT, P-COLL, and T-EAST. An observer walked each transect once every two weeks from early 1989 through 1994 recording animals observed along the transects. The data consists of species names, numbers of individuals, and perpendicular distances observed from transects, as well as weather and other context observations. Observation history and species codes are described in additional files. This study is complete.
Neural responses to naturalistic clips of behaving animals in two different task contexts
Open the record for dataset details and reuse information.
Animated caricatures fMRI study
Open the record for dataset details and reuse information.
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
Estimates of nitrogen and phosphorus excretion rates in individual marine and estuarine animals
This dataset contains nitrogen and phosphorus excretion rate, as well as dry biomass, estimates for individual vertebrate and invertebrate animals in marine and estuarine environments. This dataset is a product of an LTER Synthesis Working Group aimed at evaluating the spatiotemporal variability in consumer nutrient dynamics in the wake of global change across eight long-term ecological research projects. These projects include seven long-term ecological research programs (LTER) funded by the National Science Foundation: (1) California Current Ecosystem, (2) Florida Coastal Everglades, (3) Moorea Coral Reef, (4) Northern Gulf of Alaska, (5) Plum Island Ecosystems, (6) Santa Barbara Coastal, and (7) Virginia Coast Reserve LTER projects. Additionally, the dataset includes data from (8) The Partnership for Interdisciplinary Science of Coastal Oceans (PISCO) research program. The temporal coverage of each time series data varies among projects, with the earliest record in 1997 and the most recent in 2023. This data package also includes two folders of R scripts used for data harmonization, identical to those in the LTER Synthesis Working Group: Consumer-Mediated Nutrient Dynamics Project, v2.0.0. You can find the release in GitHub here: https://github.com/lter/lterwg-marine-cnd/releases/tag/v2.0.0
Floral traits of animal-pollinated Sevilleta plant species
Concern about pollinator populations is widespread, with bees documented to be in decline due to factors including habitat loss, disease, and pesticides. In addition, climate change may be an important cause of bee population losses, but few studies have examined bee abundance relationships with climate variables. Importantly, bees may respond directly to climate or may exhibit indirect responses to climate via changes in plant phenology or community composition. This study collected floral trait data to complement the Sevilleta LTER pollinator monitoring, plant phenology, and plant biomass datasets, with the aim of examining whether floral resource availability mediates bee responses to climate. For 71 common, animal-pollinated flowering plant species, we measured floral traits relevant to pollination in June–October 2018 and April–August 2019 within sites representing four ecosystem types at the Sevilleta National Wildlife Refuge: Plains grassland, Chihuahuan Desert grassland, Chihuahuan Desert shrubland, and piñon-juniper woodland. On a minimum of 5 individuals per plant species, we recorded the total number of open flowers and the corolla width of flowers, along with plant height and vegetative cover. These data may be used in combination with the Sevilleta LTER pollinator monitoring, phenology, and biomass datasets to examine how bee and floral resource abundance, diversity, and phenology vary across years and whether these changes correspond with one another, as well as to consider relationships among climate, floral resource abundance/diversity, and bee abundance/diversity.
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.5. Pottery (A, C), animal bones (B), a human skull (C, D), and a flint tool (D) excavated from underneath the stone layer in Kaliszany (archaeological site no. 3)
<p>The set contains a figure, with with photographs that show examples of finds discovered during excavations at archaeological site 3 in Kaliszany, Wągrowiec commune, Poland. It is a stone and earth structure in which a hoard of metal objects dating to the Late Bronze Age was discovered in 1943. The photo is from the 2022 survey, when the south-western part of the structure was explored. <br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
A Bayesian Machine Learning Framework for Animal Telemetry Data
<p>The data and tutorial in this repository are intended to be used in conjunction with the tutorial with our manuscript titled "A Bayesian Machine Learning Framework for Animal Telemetry Data." Telemetry data for three lesser prairie-chickens are provided here as .csv files. For more information about the data, please refer to our manuscript or contact Andrew Whetten or David Haukos for more information.</p>
SIA-BRA: The carbon and nitrogen stable isotope ratios of animals of Brazilian biomes and coastal marine areas
<p>SIA-BRA is a compilation of C and N stable isotope ratios of terrestrial and aquatic animals sampled in Brazilian biomes and coastal-marine areas.</p> <p>Version 1.0 contains isotopic data of c. 21,804 non-captive wildlife specimens, excluding livestock production or laboratory<br> experiments. They were 13,881 vertebrates and 7,923 invertebrates. There are 11 phyla, with a clear dominance of Chordata (64%) and Arthropoda (29%), 36 classes, 154 orders, 473 families, 894 genera and 1,157 species.</p> <p>They were divided into the following habitats: terrestrial (30% of the total), freshwater (27%), oceanic (40%)<br> and estuarine (4%) (see <a href="https://doi.org/10.1111/geb.13449">https://doi.org/10.1111/geb.13449</a>)</p> <p>Software format: Data are supplied as delimited text files (.csv).</p>
Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'
<p><strong>Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'</strong></p> <p>This dataset contains the Supporting Information of the publication </p> <p>Rühr PT & Blanke A <strong>(2022)</strong>: 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>.</p> <p>It includes</p> <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> <p>The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).</p>
Supplemental Material to "Tenacity of Animal Disease Viruses on Wood Surfaces Relevant to Animal Husbandry"
<p>Data set for individual titre reduction of viruses over a period of time in multiple experiments.</p>
Supplementary Table S27.1: Animal species native to South Africa that have invasive populations elsewhere.
<p>Animal species native to South Africa that have invasive populations elsewhere. Sorted by expected chronological appearance in the first place they were recorded as alien species. Notes are made on whether the introduction is known to be (Y) or not (N) from South Africa (or unknown U). Pathways are according to the CBD pathway classification scheme (Harrower et al. 2017), along with an indication of whether the introduction was intentional or accidental. Species that have multi-continental distributions, and which may in addition have some introduced populations are shown at the end of the table.</p>
Animal bones from Iron Age settlements in Scania, Southern Sweden
<p>This data is a compilation of the zooarchaeological record from Iron Age settlements in Scania, southern Sweden. It consists of data from various technical reports produced between 1961 to 2019, by different analysts. Published reports and unpublished but archived communications are included. This data may be of interest to anyone interested in archaeological themes involving animals in any kind, such as economy, animal husbandry, animal production, hunting, fishing, and so on. It may also be of paleozoological interest, as it contains valuable fauna historical information such as presence of wild species of different kinds. </p> <p>The database is the basis for the published catalogue included in the book "Animal husbandry in Iron Age Scania, with a catalogue" published 2022. The book is open acess and you can download it via this link: https://www.ht.lu.se/en/series/9128370/</p> <p>The data can bee accessed through a one .csv-file, which is an export of the data set which was originally recorded in a MS Access-database. Both files are published in this version. The dataset consists of data on 130 animal bone assemblages from 101 Scanian settlement sites.</p> <p>The original Access-database, with two levels, one (Site) with descriptive information on the archaeological site (totally 12 variables), and one (zooarch-overview) with quantitative data on number of specimens, in general and per recorded taxa (totally 35 variables). Presence of bird, fish, amphibian and wild mammalian taxa is also included. </p> <p>Included is a READ ME (.csv) describing the data set in more detail.</p> <p>ERRATA (READ ME-file): No of observations is 130, not 131.</p>
LukProt - an animal evolution-centric eukaryotic protein database
<p>LukProt is the EukProt database with additional species added, mostly the undersampled animal and some holozoan taxa. The database is composed of sequences translated from annotated genomes, transcriptomes or ESTs. <strong>The main purposes of the database are to consolidate sequences from undersampled animal taxa</strong> and provide usable search tools. The publication associated with LukProt can be found here: <a href="https://doi.org/10.1093/gbe/evae231">https://doi.org/10.1093/gbe/evae231</a>.</p> <p>The current version of the database (v1.5.1) is based on <a href="https://doi.org/10.24072/pcjournal.173">EukProt v3</a>. The home of all public versions of LukProt is this page (Zenodo).</p> <p>Proteomes that are novel in LukProt are denoted as LPXXXXX and those coming from AniProtDB are called APXXXXX. The sequence IDs from EukProt are conserved in LukProt. This means that each sequence is assigned an ID in the following format:</p> <pre><code>(A/E/L)PXXXXX_Species_epithet_(strain)_PYYYYYY</code></pre> <p>where XXXXX is a number from 00001 to 99999 and YYYYYY is a number from 000001 to 999999. Each sequence is assigned a unique number YYYYYY, and each taxon XXXXXX. All the IDs are compatible with BLAST v5 "-parse_seqids" option and the database can be readily deployed, for example on a server running <a href="https://doi.org/10.1093/molbev/msz185">SequenceServer</a>. Within each of the source fasta files, the source sequence identifier was kept after a blank space, so that it can still be retrieved if needed.</p> <p>A publicly available BLAST server providing LukProt search is available at: <a title="LukProt BLAST server" href="https://lukprot.hirszfeld.pl/" target="_blank" rel="noopener">https://lukprot.hirszfeld.pl/</a>.</p> <p>Comparison of EukProt v2/v3, LukProt 1.4.1 and LukProt v1.5.1 in their main areas of difference:</p> <table> <tbody> <tr> <th>Taxogroup</th> <th>EukProt v2</th> <th>EukProt v3</th> <th>LukProt v1.4.1</th> <th>LukProt v1.5.1</th> </tr> <tr> <th> <p>Holozoa</p> <p>(excluding Metazoa)</p> </th> <td>31</td> <td>40</td> <td>39</td> <td>43</td> </tr> <tr> <th>Ctenophora</th> <td>2</td> <td>2</td> <td>35</td> <td>38</td> </tr> <tr> <th>Porifera</th> <td>4</td> <td>5</td> <td>30</td> <td>47</td> </tr> <tr> <th>Placozoa</th> <td>2</td> <td>2</td> <td>3</td> <td>6</td> </tr> <tr> <th>Cnidaria</th> <td>3</td> <td>5</td> <td>65</td> <td>88</td> </tr> <tr> <th>Bilateria</th> <td>51</td> <td>51</td> <td>94</td> <td>142</td> </tr> </tbody> </table> <p>Included with the database are:</p> <ul> <li>ready to use main database files: <ul> <li><em>LukProt_v1.5.1_single_species_FASTA.7z</em> – a FASTA file with the sequences - <a href="https://en.wikipedia.org/wiki/7z">7-zipped</a>, <strong>uncompressed size: 17.6 GB</strong><br> <ul> <li>to concatenate all into one file, run this in the parent directory: <code>for file in $(find . -type f -name "*.fasta"); do awk 'FNR==1{print ""}1' $file >> LukProt_v1.5.1.fa; done</code>. This will create single FASTA file with all the sequences in the parent directory. <code>awk</code> is used to insert a new line after every file because <code>cat</code> would sometimes merge the last sequence with the header of the first sequence.</li> </ul> </li> <li><em>LukProt_v1.5.1_full_BLAST_db.7z</em> – a preformatted, full BLAST database (NCBI BLAST database format version: v5, masked with segmasker), <strong>uncompressed size: 28.3 GB</strong></li> <li><em>LukProt_v1.5.1_taxogroup_BLAST_db.7z</em> – a collection of BLAST databases where each proteome is one taxogroup and is placed within the eukaryotic tree of life directory structure, <strong>uncompressed size: 26.3 GB</strong></li> <li><em>LukProt_v1.5.1_single_species_BLAST_db.7z</em> – a collection of BLAST databases where each proteome is one BLAST database and is placed within the eukaryotic tree of life directory structure, <strong>uncompressed size: 26.4 GB</strong></li> </ul> </li> <li>auxiliary database files: <ul> <li><em>LukProt_v1.5.1.cdhit70.7z</em> – the full database clustered at 70% identity using CD-HIT with the following command: <code>cd-hit -g 1 -d 0 -T 20 -M 90000 -c 0.7 -uL 0.2 -uS 0.9 -s 0.2</code>, <strong>uncompressed sizes: fasta file - 11 GB, clstr file - 2.5 GB</strong></li> <li><em>LukProt_IDs_mapped.txt.gz</em> – a text file mapping the LukProt IDs to the AniProtDB IDs and EukProt IDs that are different</li> <li><em>BUSCO_tables.ods</em> – a spreadsheet with full result tables generated by BUSCO analysis</li> <li><em>OMAmer_output.zip</em> – a folder with full results of OMAmer analyses (includes per-sequence taxonomy classification)</li> <li><em>OMArk_output.zip</em> – a folder with the results of all OMArk analyses</li> </ul> </li> <li>metadata: <ul> <li><em>README.md</em> – a README file describing the metadata</li> <li><strong><em>LukProt_metadata_sheet.ods</em> – main metadata file. A spreadsheet with information about each proteome (in an open .ods format, most compatible with <a href="https://www.libreoffice.org/">LibreOffice</a>)</strong></li> <li><em>LukProt_metadata_other.zip</em> – an archive with other metadata files, documented in the README. Contents include:<br> <ul> <li>the LukProt taxonomy in various formats</li> <li>supporting scripts for data manipulation and visualization</li> </ul> </li> <li>a recoloring script (modified by LFS, originally by Dr. Celine Petitjean). The script is in <a title="formatFigtree2" href="https://doi.org/10.5281/zenodo.10654583">public domain</a> and reuploaded here only for convenience. </li> <li>other files - see README</li> </ul> </li> <li><em>changelog.md</em> – database changelog</li> </ul> <p>Words of caution:</p> <ul> <li>The database has been synchronized to EukProt v3 in version v1.5.1. This means that identifiers were modified in comparison to LukProt v1.4.1. The convention is not expected to change any more in future updates.</li> <li>Many proteomes, especially those transcriptome-based, may contain contamination from different species. In addition, the translation algorithms often introduce errors (e.g. the transcript may not represent a full length protein). For this reason, to get accurate sequences from each organism, users are directed to source data and to the included OMAmer, OMArk and BUSCO data for details.</li> <li>The taxonomy is different to UniEuk/EukMap, but UniEuk data were integrated where possible.</li> <li>A few NCBI taxids are missing and will be added in due course.</li> <li>Proteomes from NCBI and UniProt will be updated to current versions.</li> <li>A number of proteomes present in some metadata, are unpublished and were held back.</li> <li>While the database contains metadata that present a particular phylogeny of animals, holozoans and other eukaryotes, no particular claims or hypotheses are made by the author(s). However, in the future efforts will be made to name clades officially, once they are more firmly established.</li> </ul> <p><strong>Please report any problems or suggestions to Lukasz Sobala: lukasz.sobala (at) hirszfeld.pl.</strong></p> <p> </p> <p>Acknowledgements:</p> <ul> <li> <p>Andrew E. Allen Lab for creating the original <a href="https://allenlab.ucsd.edu/data/" target="_blank" rel="noopener">PhyloDB</a>.</p> </li> <li> <p>Daniel Richter <em>et al.</em> for creating <a href="https://doi.org/10.6084/m9.figshare.12417881">EukProt</a> and keeping it updated.</p> </li> <li> <p>Members of <a href="https://multicellgenome.com/">the Multicellgenome Lab</a>, especially Michelle Leger (for donating her database), for the bioinformatics support and for doing great science.</p> </li> <li> <p>All the authors of the original data.</p> </li> <li> <p>National Science Centre of Poland for funding of the project 2020/36/C/NZ8/00081, "The role of glycosylation in the emergence of animal multicellularity", which enabled the creation of this database.</p> </li> </ul>
Indicative distribution map for Ecosystem Functional Group M1.5 Photo-limited marine animal forests
<p>This archive contains indicative distribution maps and profiles for <strong>M1.5 Photo-limited marine animal forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Animation to visualize the electron beam damage induced in calcium silicate hydrate phases
<p>This dataset visualizes the electron beam damage induced by a scanning electron microscope (SEM) in calcium silicate hydrates (C-S-H). The specimen used is 28 days hydrated alite (water/solid = 0.5). It was scanned using a thermofischer scientific Helios G4 UX microscope at 350 V/25 pA with a stage bias of 200 V.</p> <p>This animation was an afterthought. Therefore, the dataset provides multiple magnifications and resolutions and some of the images are not in focus. Nevertheless, It can be seen, that the C-S-H needle in the right half of the image significantly deformes within a timespan of 124 seconds of constant scanning of that region.</p> <p><strong>File content:</strong></p> <ul> <li>All images ending with "raw" are the raw images provided by the SEM software including all metadata.</li> <li>The file "C3S_CSH_e-beam-damage_aligned stack.tif" contains the aligned image set using the SIFT algorithm. It contains the correct scaling if opened with ImageJ.</li> <li>The file "C3S_CSH_e-beam-damage_animation.gif" provides the final animation including a overlayed scalebar.</li> </ul>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466</p> <p>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</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.