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36,930 results for “Humanities”
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong></p> <p>Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.</p>
Diffraction images used to solve the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target"
<p>Raw diffraction images used for generating the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target" (available <a href="https://doi.org/10.1073/pnas.2013620117">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. Datasets 6ZJ1 and 6ZJ5 were cut anisotropically using STARANISO, other datasets were processed isotropically.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Phindr3D: Test Data Set 2 (human MCF10A breast cancer organoids)
<p>3D confocal image stacks of human MCF10A breast cancer organoids expressing different oncogenes to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP files.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p>
Diffraction images of crystals of the first and second spectrin repeats (mutant C420A/C435A) of human plectin (PDB code 2ODV): 2-wavelength SeMet MAD dataset
<p>Diffraction images of SeMet labeled crystals of a fragment of human plectin that includes the first and second spectrin repeats (SR1-SR2) of the plakin domain. The two Cys in the wild type sequence were replaced by Ala.</p> <p>This Se-Met MAD dataset was used for the <em>de novo</em> phasing of the pdb entry 2ODV (http://www.rcsb.org/pdb/explore/explore.do?structureId=2ODV).</p> <p> </p> <p>Data was collected at the BM14 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using a Mar CCD detector. Data from the same crystal were collected at two wavelengths :</p> <ul> <li>Remote wavelength (0.9185 Å): 180 images (1 degree oscillation per image).</li> <li>Peak wavelength ( 0.9785 Å): 360 images (1 degree oscillation per image).</li> </ul>
Background data: Untangling the effects of multiple human stressors and their impacts on fish assemblages in European running waters
<p>This dataset presents some backkground data from the EFI+ database. Related work addresses human stressors and their impacts on fish assemblages at pan-European scale by analysing single and multiple stressors and their interactions. Based on an extensive dataset with 3105 fish sampling sites, patterns of stressors, their combination and nature of interactions, i.e. synergistic, antagonistic and additive were investigated. </p> <p>Data were derived within the EU-project "Improvement and Spatial extension of the European Fish Index (EFI+)". EFI+, an EU FP6 research project from 2007-2009 was designed to gain new knowledge and to further develop and improve new biological assessment methods to meet needs of the Water Framework Directive (WFD). </p> <p>Background data are available for boxplots and barplots shown in the related research article in STOTEN.</p>
Human intestinal Bacteria Collection (HiBC): Isolates and genomes metadata
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the taxonomy of the isolates, as well as metadata regarding their cultivation and isolation. We also provide metadata regarding the sequencing, genome assembly process and the biological sequences.</p> <p><strong>UPDATE v7</strong>: INSDC accession for <em>Segatella sinensis</em> CLA-AA-H117 was a missing value and is now the correct value of GCA_040324585.2.</p> <p><strong>UPDATE v6: </strong>The growth atmosphere is now indicated by anaerobic or aerobic instead of "Anaerobe/Aerobe" that was a misleading term. The risk group of these two isolates went from 1 to 2:</p> <ul> <li>CLA-AA-H205: <em>Anaerostipes caccae </em></li> <li>CLA-AA-H83: <em>Bacteroides fragilis</em></li> </ul> <p>The risk group of the following isolates has been updated (usually from unknown to 1, or from 2 to 1):</p> <ul> <li>CLA-SR-H026: <em>Aedoeadaptatus acetigenes</em></li> <li>CLA-KB-H139:<em> Bacteroides xylanisolvens</em></li> <li>CLA-SR-H015: <em>Bacteroides xylanisolvens</em></li> <li>CLA-AA-H187: <em>Blautia fusiformis</em></li> <li>CLA-AA-H274: <em>Brotaphodocola catenula</em></li> <li>CLA-AA-H286: <em>Butyricimonas faecihominis</em></li> <li>CLA-AA-H278:<em> Clostridium fessum</em></li> <li>CLA-AA-H147: <em>Dorea ammoniilytica</em></li> <li>CLA-SR-H027: D<em>orea formicigenerans</em></li> <li>CLA-KB-H89: <em>Dorea longicatena</em></li> <li>CLA-KB-H94: <em>Dorea longicatena</em></li> <li>CLA-SR-H022: <em>Enterococcus lactis</em></li> <li>CLA-AA-H250: <em>Hominenteromicrobium mulieris</em></li> <li>CLA-AA-H232: H<em>ominilimicola fabiformis</em></li> <li>CLA-AA-H246: <em>Hominisplanchenecus faecis</em></li> <li>CLA-AA-H276:<em> Hominiventricola filiformis</em></li> <li>CLA-AA-H213:<em> Oliverpabstia intestinalis</em></li> <li>CLA-AA-H241: <em>Oliverpabstia intestinalis</em></li> <li>CLA-AA-H58: <em>Pilosibacter fragilis</em></li> <li>CLA-KB-H110: <em>Ruthenibacterium lactatiformans</em></li> <li>CLA-AA-H174: <em>Segatella sinensis</em></li> <li>CLA-AA-H2: <em>Veillonella parvula</em></li> <li>CLA-AA-H273: <em>Waltera acetigignens</em></li> </ul> <p>Typos in media list have been fixed. </p> <p><strong>UPDATE v5</strong>: The accessions number for the genomes on INSDC databases are added under the column Accession. Plus two typos in the risk group column have been corrected as follow:</p> <ul> <li>CLA-AA-H173: from Risk Group 4 (!) to 2 like the other strain of <em>Sutterella wadsworthensis</em></li> <li>CLA-AA-H198: from Risk Group 4 (!) to 1 like the other <em>Bifidobacterium </em>species.</li> </ul> <p><strong>UPDATE v4</strong>: Only the taxonomy of a couple of isolates has been changed, as follow:</p> <ul> <li>CLA-ER-H4: <em>Collinsella sp900547855</em> instead of <em>Collinsella sp900544645</em></li> <li>CLA-AA-H142: <em>Pilosibacter fragilis</em> (<em>f__Clostridiaceae</em>) instead of <em>Sakamotonia hominis gen. nov.</em> (<em>f__Lachnospiraceae</em>)</li> <li>CLA-AA-H58: <em>Pilosibacter fragilis </em>(<em>f__Clostridiaceae</em>) instead of <em>Sakamotonia hominis gen. nov. </em>(<em>f__Lachnospiraceae</em>)</li> <li>CLA-AA-H89B: <em>Lachnospira intestinalis sp. nov.</em> instead of <em>Lachnospira hominis sp. nov.</em></li> <li>CLA-JM-H10: <em>Lachnospira hominis sp. nov.</em> instead of <em>Lachnospira intestinalis sp. nov.</em></li> <li>CLA-JM-H7B: <em>Faecalibacterium taiwanense</em> instead of <em>Faecalibacterium faecis sp. nov.</em></li> <li>CLA-JM-H45: <em>Merdimmobilis hominis</em> instead of <em>Hominicola intestinalis gen. nov.</em></li> </ul> <p><strong>UPDATE v3</strong>: The genome of one of our isolate had been unfortunately swapped. This mistake has been now corrected on Zenodo and Coscine. The genome of <em>Segatella sinensis</em> CLA-AA-H117 should be considered correct with 103 contigs and 3 671 232 nt. Please note that the genome available at the NCBI is the correct one (GCA_040324585.2). Two typos regarding taxonomy have been corrected as well: <em>Maccoya intestinihominis</em> has been corrected to <em>Maccoyia intestinihominis</em> and <em>Faecousia faecis</em> to <em>Faecousia intestinalis</em>.</p>
The Human Niche Space of Post-LGM Late Upper Paleolithic Europe - Supplemental Material
<p>The data provided here are the supplemental information accompanying the journal article <strong>The Human Niche Space of Post-LGM Late Upper Paleolithic Europe: The Effects of Climate and Population Growth on Human Land Use</strong> by Yaworsky, Hussain, & Riede. All analyses were performed in R v4.5.0 and are documented in the HTML document, <strong>Supplemental 4</strong>.</p> <p>Version 1.2 of the Analysis Markdown Document incoporates changes made to functions within the package ENMeval.</p> <p>List of Supplemental Files:</p> <ol> <li><strong>Spatiotemporal Archaeological Observations - File name: <em>Archaeologicaldata_v1.csv</em></strong> <ol> <li>Archaeological observations derived from Kretschmer (2015) and supplemented with additional observations (see main paper for details).</li> </ol> </li> <li><strong>Summed Probability Estimate for Population Estimation - File name: <em>Population_SPD2.csv</em></strong><br> <ol> <li>Summed probability distribution estimating changes in relative population size across Europe from 22ka ago to 9.1ka ago using data from the P3K14C database (Bird et al, 2022; see <strong>Supplemental 4</strong> for details).</li> </ol> </li> <li><strong>Spatiotemporal Background Points - File name: </strong><em><strong>AbsencePointData.csv</strong></em><br> <ol> <li>Randomly generated background points. 100 random points were generated in each millennium.</li> </ol> </li> <li><strong>Analysis Markdown Document - File Name: </strong><em><strong>CLIOARCH_MD_v1.2.html</strong></em><br> <ol> <li>Markdown illustrating step-by-step the methods used to organize and analyze the data.</li> </ol> </li> <li><strong>High-Resolution Spatiotemporal Predictions - File name: </strong><em><strong>SDM_MainGIF.mp4</strong></em><br> <ol> <li>High-resolution mp4 file showing the predictions of the potential climate niche space for humans from 22ka to 9.1ka ago.</li> </ol> </li> <li><strong>Potential Niche Space 22ka to 9.1ka ago- File name: </strong><em><strong>Human_Niche_Size.csv</strong></em> <ol> <li>Quantification of the potential climate niche space for each century.</li> </ol> </li> </ol> <p>The Climate data are not provided due to their size but are sourced from Karger et al (2023) and are accessible <a href="https://chelsa-climate.org/">here (https://chelsa-climate.org/)</a>.</p> <p> </p>
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
Taxonomic list of Brazilian fruit-bearing plants for human use
<h3>Lista taxonômica de plantas frutíferas para consumo humano, com curadoria da equipe do projeto <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a>. </h3> <p><em>[see English description below]</em></p> <p><br>As planilhas estão organizadas da seguinte forma:</p> <p><strong>PT_lista_especies_aceitas_v.3.0</strong>: contém os nomes de todas as espécies atualmente indexadas na base de dados do <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a>.</p> <p><strong>PT_lista_especies_adicionadas_v.3.0</strong>: contém os nomes das novas espécies que passam a integrar a base de dados do Pomar Urbano a partir da versão 3.0.</p> <p><strong>PT_lista_especies_removidas_v3.0</strong>: contém os nomes das espécies removidas da versão 3.0 da lista, e que portanto não fazem mais parte do banco de dados do projeto. </p> <p> </p> <p><strong>Metadados usados nas planilhas:</strong></p> <ul> <li><em>Nome científico</em>: O nome científico completo, com autoria e data, se conhecidos.</li> <li><em>Família</em>: O nome científico completo da família.</li> <li><em>Nome vernacular</em>: nome comum, popular.</li> <li><em>Origem</em><strong>: </strong>Declaração sobre se um organismo foi introduzido em um local e tempo específicos por meio da atividade direta ou indireta dos seres humanos modernos.</li> <li><em>Distribuição geográfica</em>: área geográfica ou região onde uma espécie ocorre no Brasil. Foram considerados como valores válidos para este campo apenas as macrorregiões do Brazil, a saber: S = Sul, SE = Sudeste, CO = Centro-Oeste, NE = Nordeste, N = Norte.</li> <li><em>Última atualização</em>: A data mais recente em que a entrada no catálogo foi alterada, atualizada ou modificada.</li> </ul> <h3>--------------------------------------------------------------------------------------------------------------------------------------<br><br>Taxonomic list of fruit-bearing plants for human consumption, curated by the <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano project</a></h3> <p><em>[Vernacular names are presented only in Portuguese; for properly processing in data management tools, downloading a Portuguese language package might be necessary]</em></p> <p>The spreadsheets are organized as follows:</p> <p>EN_list_accepted_species_v.3.0: contains the names of all species currently indexed in the <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a> database.</p> <p>EN_new_added_species_v.3.0: contains the names of new species that are included in the Pomar Urbano database starting from version 3.0.</p> <p>EN_removed_species_v.3.0: contains the names of species that were present in the version 2.0 of the list and are therefore no longer part of the version 3.</p> <p> </p> <p><strong>Metadata used in the spreadsheets</strong>:</p> <p><em>Scientific Name</em>: The complete scientific name, including authorship and date, if known. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/scientificName">dwc:scientificName</a>. </p> <p><em>Family</em>: The full scientific name of the family. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/family">dwc:family.</a></p> <p><em>Vernacular Name</em>: Common or popular name. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/vernacularName">dwc:vernacularName</a></p> <p><em>Establishment Means</em>: Statement about whether an organism has been introduced to a specific place and time through the direct or indirect activity of modern humans. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/establishmentMeans">dwc:establishmentMeans</a></p> <p><em>Higher geography</em>: The geographical area or region where a species occurs in Brazil. Only the macroregions of Brazil are considered valid values for this field within this dataset, namely: S = South, SE = Southeast, CO = Central-West, NE = Northeast, N = North. <em>CloseMacth</em>: <a href="http://rs.tdwg.org/dwc/terms/higherGeography">dwc:higherGeography</a></p> <p><em>Last Update</em>: The most recent date on which the catalog entry was changed, updated, or modified. <em>ExactMatch</em>: <a href="http://purl.org/dc/terms/modified">dct:modified</a></p> <p> </p>
Collection of spatial information and maps of human past and environment in the Uralic languages speaker area
<p>The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a> - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.</p>
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>
Unified Human Gastrointestinal Proteome clustering results by DPCfam
<p>This dataset contains the result of clustering the Unified Human Gastrointestinal Proteome (UHGP) using the DPCfam algorithm. </p> <p>More details on the DPCfam clustering algorithm can be found in the original publication:</p> <p>Russo, Elena Tea, et al. "DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets." <em>PLOS Computational Biology</em> 18.10 (2022): e1010610. <a href="https://doi.org/10.1371/journal.pcbi.1010610">https://doi.org/10.1371/journal.pcbi.1010610</a></p> <p>All of the putative protein families obtained through DPCfam (including previous results) can be browsed online at our dedicated webserver: <a href="https://dpcfam.areasciencepark.it/uhgp">https://dpcfam.areasciencepark.it/uhgp</a></p> <p>The original protein dataset is version 1.0 of the UHGP-50 dataset, available for download from MGnify at <a href="https://www.ebi.ac.uk/metagenomics/.">https://www.ebi.ac.uk/metagenomics/</a>.</p> <p><strong>FILES DESCRIPTION:</strong></p> <p>Only MCs with seeds with 1) more than 50 elements and 2) average length larger than 50 aminoacids are reported.</p> <p><strong>metaclusters_xml.tar.gz:</strong></p> <ul> <li><strong>dpcfam_uhgp_metaclusters.xml</strong>: Metaclusters' seeds. Metaclusters entries include also some statistical information about each MC (such as size, average length, low complexity fraction, etc.) and Pfam comparison (Dominant Architecture).</li> <li><strong>dpcfam_metaclusters.xsd</strong>: XML schema file for the data. </li> <li><strong>MCxml_to_tables.awk:</strong> Awk script to convert from XML to tabular text files. Use through the parse.sh script.</li> <li><strong>parse.sh</strong>: XML parser. </li> <li><strong>README.md</strong></li> </ul> <p><strong>uhgp_xml.tar.gz: </strong></p> <ul> <li><strong>uhgp_seed_match.xml</strong>: XML file containing all of UHGP-50 proteins and its corresponding sequences, annotated with Pfam and DPCfam metacluster data. Annotations comprise the membership of a protein as a seed or matches found though the profile-hmms of the DPCfam-UHGP and the DPCfam-Uniref clusterings. </li> <li><strong>uhgp_matches.xsd</strong>: XML schema file for the data. </li> <li><strong>xml_to_list.awk:</strong> Awk script to convert from XML to tabular text files. Use through the parse.sh script.</li> <li><strong>xml_to_list_mcfiles.awk:</strong> Awk script to convert from XML to tabular text files (including individual files for metaclusters' seeds). Use through the parse.sh script.</li> <li><strong>parse.sh</strong>: XML parser. </li> <li><strong>README.md</strong></li> </ul> <p><strong>Metacluster Files:</strong></p> <ul> <li><strong>seeds.zip: </strong>Metaclusters' seed sequences. A fasta file for each metacluster before filtering.</li> <li><strong>filtered_seeds.zip: </strong>Metaclusters' seed sequences after clustering at 60 percent identity. </li> <li><strong>metaclusters_hmms.tar.gz: </strong>Metaclusters' profile-hmms. A ".hmm" file for each metacluser. </li> <li><strong>metaclusters_msas.tar.gz: </strong>Metaclusters' multiple sequence alignments, in fasta format. </li> </ul> <p><strong>uhgp_protein_mapping.txt:</strong></p> <ul> <li>Contains a mapping between the identifiers of versions 1.0 and 2.0.2 of UHGP. The first column corresponds to the ID in UHGP-50 1.0 (representatives for the clustering at 50% protein identity), the second column to the ID in version 2.0.2 and the third column to the ID of the representative of the protein for clustering at 100% sequence identity, for which the protein sequence can be found in UHGP-100. </li> </ul>
Attention-based frontal-posterior coupling for visual consciousness in the human brain
<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively. </li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image. </li> </ul> </li> </ol> <p> </p>
Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions
<p>This Zenodo entry contains the supplementary material associated with the manuscript titled <em>Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99.</em> The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024.</p> <p>A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well. </p> <p>[1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." <em>Mendeley Data</em>, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.</p>
Data to "Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli"
<p>This record contains analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Dehn, K.<strong>†</strong>, Maiello, G.<strong>†</strong>, Hartmann, F., Morgenstern, Y., Hawkins, S.J., Offner, T., Walter, J., Hassenklöver, T., Manzini, I., Fleming, R.W. (2024) Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli. bioRxiv, 2024-12. https://doi.org/10.1101/2024.12.21.629735 </p> <p><em><strong>†</strong>Co-first author</em></p>
Human Microbiome Compendium dataset
<p>The Human Microbiome Compendium is an ongoing project to build a large collection of human microbiome sequencing data processed with a uniform pipeline. Currently, the compendium contains 16S rRNA amplicon sequencing data for human gut microbiome samples retrieved from the Sequence Read Archive. Our website at <strong><a href="https://microbiomap.org">microbiomap.org</a></strong> has more information about the project and links to related resources.</p> <p>This data is freely available under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International license</a> (<strong>CC BY 4.0</strong>). If you use it in your work, please cite our publication:</p> <p>Abdill, Richard J., Samantha P. Graham, Vincent Rubinetti, et al. “Integration of 168,000 Samples Reveals Global Patterns of the Human Gut Microbiome.” Cell 188, no. 4 (2025): 1100–18. <a href="https://doi.org/10.1016/j.cell.2024.12.017" target="_blank" rel="noopener">https://doi.org/10.1016/j.cell.2024.12.017</a></p> <p>If you are using this dataset in combination with your own results, it's important to note that the taxonomic classifications may differ between releases, as documented in CHANGELOG.md. The most recent release (1.1.1) includes assignments made using <strong><a href="https://www.arb-silva.de/news/view/2024/07/11/silva-release-1382/">SILVA 138.2</a></strong> (SSU Ref NR 99) and <strong>Greengenes2</strong> (2022.10 backbone).</p>
AnDy suit: human weight lifting wearable data
<p>This dataset comprises wearable data, collected using <a href="https://andy-project.eu/results/andysuit">An.Dy. suit</a>, from two weight lifting experiments of a human subject. Wearable data include kinematic measurements acquired with the <a href="https://www.xsens.com/">Xsens Motion Tracking system</a> (composed by 17 IMUs) and <a href="https://ifeeltech.eu/">iFeel shoes</a> (force/torque sensorized shoes developed by Istituto Italiano di Tecnologia).</p> <p>The experimental design is the following:</p> <p><strong>Experiment 01</strong></p> <p>Lifting task Geometry, accordingly to NIOSH convention:</p> <ul> <li>H = 63 cm</li> <li>V = 30 cm</li> <li>D = 40 cm</li> <li>CM = 0.9</li> <li>Load = 7 kg</li> </ul> <p>The task is executed 10 times.</p> <p><strong>Experiment 02</strong></p> <p>Lifting task Geometry, accordingly to NIOSH convention:</p> <ul> <li>H = 31 cm</li> <li>V = 66 cm</li> <li>D = 42 cm</li> <li>CM = 1</li> <li>Load = 5 kg</li> </ul> <p>The task has been executed:</p> <ul> <li>5 minutes: Lifting with back only</li> <li>5 minutes: Lifting with back plus leg</li> </ul> <p><strong>Data Structure</strong></p> <p>Data structure is the following:</p> <p>- experiment0x</p> <p> - wearable_data</p> <p> - FTshoes</p> <p> - xsens</p> <p>- subject_model</p> <p> </p> <p><strong>Data Interpretation</strong></p> <p>Data have been collected using <a href="https://www.yarp.it//v3.5/yarpdatadumper.html">YARP datadumper tool</a> using the thrift message implemented in <a href="https://github.com/robotology/wearables">wearables library</a>.</p> <p><strong>Data Usage</strong></p> <p>Data can be used by <a href="https://github.com/robotology/human-dynamics-estimation">human-dynamics-estimation</a> devices for replicating the results presented in:</p> <ul> <li>Rapetti, L.; Tirupachuri, Y.; Darvish, K.; Dafarra, S.; Nava, G.; Latella, C.; Pucci, D. Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics. <em>Algorithms</em> 2020, <em>13</em>, 266. https://doi.org/10.3390/a13100266</li> <li>Latella, C.; Traversaro, S.; Ferigo, D.; Tirupachuri, Y.; Rapetti, L.; Andrade Chavez, F.J.; Nori, F.; Pucci, D. Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques. <em>Sensors</em> 2019, <em>19</em>, 2794. https://doi.org/10.3390/s19122794</li> <li>Tirupachuri, Y. ; Ramadoss, P. ; Rapetti, L. ; Latella, C. ; Darvish, K. ; Traversaro, S. ; Pucci D. Online Non- Collocated Estimation of Payload and Articular Stress for Real-Time Human Ergonomy Assessment. <em>IEEE Access</em>, <em>pp. 1–1, Aug. </em>2021, https://ieeexplore.ieee.org/document/9526592.</li> </ul> <p> </p>
Inactive to active transition of human Thymidine Kinase 1 revealed by Molecular Dynamics simulations
<p>The trajectories and input files for the manuscript <em>Inactive to active transition of human Thymidine</em></p> <p><em>Kinase 1 revealed by Molecular Dynamics simulations</em> (<a href="https://doi.org/10.1021/acs.jcim.1c01157">https://doi.org/10.1021/acs.jcim.1c01157</a>) </p> <p>ABSTRACT</p> <p>Despite its importance for the nucleoside (and nucleoside prodrug) metabolism, the structure<br> of the active conformation of human Thymidine Kinase 1 (hTK1) remains elusive. We perform<br> microsecond molecular dynamics simulations of the inactive enzyme form bound to a<br> bisubstrate inhibitor that was shown experimentally to activate another TK1-like kinase,<br> Thermotoga maritima TK (TmTK). Our results are in excellent agreement with the<br> experimental findings for the TmTK closed-to-open state transition. We show that the inhibitor<br> induces an increase of the enzyme radius of gyration due to the expansion on one of the dimer<br> interfaces; the structural changes observed, including the active site pocket volume increase,<br> decrease in monomer-monomer buried surface area and of the number of hydrogen bonds (as<br> compared to the inactive enzyme control simulation), show that the catalytically competent<br> (open) conformation of hTK1 can be assumed in the presence of an activating ligand.</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.