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7,324 results for “pathway”

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edi60/100

Hydraulic Pathways in Leaves of Temperate Trees at Harvard Forest 2002

The transport of water, sugar and nutrients in trees is restricted to specific vascular pathways, and thus organs may be relatively isolated from one another (=sectored). Strongly sectored leaf-to-leaf pathways have been shown for the transport of sugar and signal molecules within a shoot, but not previously for water transport. The hydraulic sectoriality of leaf-to-leaf pathways was determined for current year shoots of six temperate deciduous tree species (three ring-porous: Castanea dentata, Fraxinus americana and Quercus rubra, and three diffuse-porous: Acer saccharum, Betula papyrifera and Liriodendron tulipifera). Hydraulic sectoriality was determined using dye staining and a hydraulic method. In the dye method, leaf blades were removed, and dye was forced into the most proximal petiole. For each petiole we counted the vascular traces shared with the proximal petiole. For other shoots, measurements were made of the leaf-area specific hydraulic conductivity for leaf-to-leaf pathways (kLL). In five of six species patterns of sectoriality reflected phyllotaxy; both the sharing of vascular bundles between leaves and kLL were higher for orthostichous than non-orthostichous leaf pairs. Species-differences in leaf-to-leaf sectoriality were determined as the proportional differences between non-orthostichous vs. orthostichous leaf pairs in their staining of shared vascular bundles and in their kLL; for the six species these two indices of sectoriality were strongly correlated (R2 = 0.94; P less than 0.001). Species varied 8-fold in their kLL-based sectoriality, and ring-porous species were more sectored than diffuse-porous species. Differential leaf-to-leaf sectoriality has implications for species-specific coordination of leaf gas exchange and water relations within a branch, especially during fluctuations in irradiance, water and nutrient availability.

openCC0Dec 2023View details →

OpenNeuro52/100

How ovarian hormones influence the behaviroal activation and inhibition system through the dopamine pathway

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →

zenodo52/100

Dataset for the tutorial "pinpoint key pathways with Heinz"

The dataset for the tutorial "pinpoint key pathways with Heinz" in Galaxy training network.

opencc-by-4.0Aug 2018View details →

zenodo52/100

Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"

Mehl, Thorens et al present a multiomics study aimiing to identify the pathways that are coordinately regulated in pancreatic b-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis.

opencc-by-4.0Sep 2024View details →

edi52/100

Impacts of invasive species on food web energy pathways and quality, St. Lawrence River, 2018-2021.

This dataset contains field measurements collected between 2018 and 2021 from three fluvial lakes in the Upper St. Lawrence River (Canada), including both invaded systems (with dreissenid mussels and round goby) and uninvaded reference sites. Data include georeferenced sampling information (site, lake, latitude, longitude, month, year), water chemistry (total phosphorus, µg/L; conductivity, µS/cm), and habitat descriptors (substrate). Biological records encompass seston, macroinvertebrates, and fish. Fish data comprise species identity, sex, total length (mm), weight (g), relative weight index (Wr), and detailed fatty acid composition expressed as relative proportions (%) and concentrations (µg/mg), including essential LC-PUFAs (EPA, DHA), n-3 and n-6 polyunsaturated fatty acids. Stable isotope data are provided, including carbon (δ13C) and nitrogen (δ15N) ratios, C:N ratios, and isotopic baselines from pelagic (δ13Cpel, δ15Npel) and benthic (δ13Cben, δ15Nben) sources. Derived variables, such as pelagic diet proportion and trophic position, were calculated using the two-source mixing model described by Post (2002) (DOI: https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2). These data provide a comprehensive resource for examining food web structure, energy pathways, and the ecological impacts of invasive species in large river ecosystems.

openCC (other)Sep 2025View details →

edi52/100

Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine: otolith data, model data, and post-processed model data products

This dataset includes hatch and larval period for sand lance collected in 2019 and results from particle tracking runs of simulated sand lance larvae throughout the Northeast U.S. Shelf as part of Long-Term Ecological Research (NES-LTER). Release dates vary by region, corresponding to hatch and settlement dates of settling sand lance collected in 2019. Particles were depth-keeping throughout the upper 40 m to best replicate our understanding of the vertical distribution of sand lance larvae. Data were used to determine the average particle transport pathways from these sand lance habitats, including connectivity among the three hotspots, and spatial variability of connectivity within each hotspot. Further information can be found within the manuscript: Suca, J. J., Ji, R., Baumann, H., Pham, K., Silva, T. L., Wiley, D. N., Feng, Z., & Llopiz, J. K. (2022). Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine. Fisheries Oceanography, 31( 3), 333-352. https://doi.org/10.1111/fog.12580

openCC (other)Jun 2022View details →

zenodo48/100

Alignments used in "The evolution of the phenylpropanoid pathway entailed pronounced radiations and divergences of enzyme families"

Alignments used in de Vries et al. (2021) "The evolution of the phenylpropanoid pathway entailed pronounced radiations and divergences of enzyme families" published as (1) a pre-print: https://doi.org/10.1101/2021.05.27.445924 (2) in Plant Journal (in press)

opencc-by-4.0Jun 2021View details →

zenodo48/100

Sustainable Agricultural Pathways in Europe (SIPATH) – land use data

The published land use data were part of the project “What is Sustainable Intensification? Operationalizing Sustainable Agricultural Pathways in Europe (SIPATH)”, which was funded by the Swiss National Science Foundation (grant no. CRSII5_183493). The overall objective of this project was to assess short-term trajectories in agriculture land use and landscape structure over a period of 20 years for 16 individual study sites located in 11 countries, ranging from the Mediterranean to the boreal zone. The following datasets illustrate land use data that were generated for the different study sites at two points in time between 2000 and 2020, depending on data availability. The data collection process encompassed land use mapping through visual image interpretation of orthorectified aerial photographs using geographic information systems (ESRI ArcGIS Pro). Land use digitalization was conducted by two scientists in Switzerland, who were in contact with the respective project partners in the study countries to exchange expert knowledge. Minimal mapping unit was 25m2 for areal elements. Land cover was classified following the European Nature Information System (EUNIS) habitat classification (EEA 2019). The broadest habitat classes were systematically mapped in the study sites, with each covering an area of approximately 25 km². While EUNIS focuses on habitat types, the study, however, targeted the intensity of agricultural land-use, several EUNIS classes were complemented with levels of land-use intensities. This was applied for grassland, olive groves and fruit orchards. The spatial resolution of the orthophotos ranged from 25cm to 2m. Accordingly, there were situations in which the spatial resolution was insufficient to accurately determine the land use. In such cases, either orthophotos from about the same year were consulted, possibly showing different phenological stages, or land use statistics and expert judgement based on local expert knowledge of the respective study area were applied.   Reference: EEA, 2019. EUNIS habitat classification 2007 (Revised descriptions 2012) amended 2019. Copenhagen (<a href="https://www.eea.europa.eu/ds_resolveuid/27788ca43d9e4f2e9477b15df88d20be" target="_blank" rel="noopener">Permalink</a>)

opencc-by-4.0Nov 2024View details →

zenodo48/100

Zebrafish Pathway Metabolite MetFrag Local CSV

This is a local CSV file of Zebrafish metabolites for MetFrag (https://msbi.ipb-halle.de/MetFrag/) extracted from PubChem, based partially on previous data extracted from Wikipathways, KEGG and literature (DOI: <a href="https://doi.org/10.1371/journal.pone.0213661">10.1371/journal.pone.0213661</a>), combined in previous versions of this record (DOI: <a href="https://doi.org/10.5281/zenodo.3541624">10.5281/zenodo.3541624</a>). This file was created as documented on the <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem-docs/-/tree/main/taxonomy/Danio_rerio">ECI GitLab</a>.  This file is designed for identification using MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.

opencc-by-4.0Sep 2019View details →

zenodo48/100

Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals

Supplementary Material for: Emerling C.A., Springer M.S., Gatesy J., Jones Z., Hamilton D., Xia-Zhu D., Collin M.A., and Delsuc F. (2021). Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals. Open Research Europe 1:75. doi:10.12688/openreseurope.13795.1.   Supplementary File Legends: - Supplementary_Figure_S1.pdf: AANAT PAML ‘master model’ showing branch categories, corresponding to “Model 1: 24 ratio” in Supplementary Table S7. - Supplementary_Figure_S2.pdf: ASMT PAML ‘master model’ showing branch categories, corresponding to “Model 2: 24 ratio” in Supplementary Table S8. - Supplementary_Figure_S3.pdf: MTNR1A PAML ‘master model’ showing branch categories, corresponding to “Model 1: 27 ratio” in Supplementary Table S9. - Supplementary_Figure_S4.pdf: MTNR1B PAML ‘master model’ showing branch categories, corresponding to “Model 1: 46 ratio” in Supplementary Table S10. - Supplementary_Figure_S5.pdf: RAxML AANAT gene tree. Numbers at nodes correspond to bootstrap support values. - Supplementary_Figure_S6.pdf: RAxML ASMT gene tree. Numbers at nodes correspond to bootstrap support values. - Supplementary_Figure_S7.pdf: RAxML MTNR1A+MTNR1B tree. Numbers at nodes correspond to bootstrap support values. - Supplementary_Figure_S8.pdf: Supporting data showing the inactivation of MTNR1A exon 2 in cetaceans. Read Supplementary Table S13 for further details. - Supplementary_Figure_S9.pdf: Supporting data showing the inactivation of ASMT in spalacids and Fukomys damarensis. Read Supplementary Table S13 for further details. - Supplementary_Figure_S10.pdf: Supporting data showing the inactivation of MTNR1A in hyracoids and Cyclopes didactylus. Read Supplementary Table S13 for further details. - Supplementary_Figure_S11.pdf: Supporting data showing the inactivation of MTNR1A in sirenians. Read Supplementary Table S13 for further details. - Supplementary_Figure_S12.pdf: Supporting data showing the inactivation of AANAT in sirenians and a polymorphic premature stop codon in exon 5 of ASMT in Trichechus manatus. Read Supplementary Table S13 for further details. - Supplementary_Figure_S13.pdf: Supporting data showing the inactivation of MTNR1A in Condylura cristata. Read Supplementary Table S13 for further details. - Supplementary_Figure_S14.pdf: Supporting data showing the inactivation of MTNR1A in Phataginus tricuspis. Read Supplementary Table S14 for further details. - Supplementary_Figure_S15.pdf: PAML AANAT results, Model 1: 24 ratio (see Supplementary Table S7). - Supplementary_Figure_S16.pdf: PAML ASMT results, Model 2: 24 ratio (see Supplementary Table S8). - Supplementary_Figure_S17.pdf: PAML MTNR1A results, Model 1: 27 ratio (see Supplementary Table S9). - Supplementary_Figure_S18.pdf: PAML MTNR1B results, Model 1: 46 ratio (see Supplementary Table S10). - Supplementary_Table_S1.xlsx: List of species examined in this study and the sources of the genes. Source key: WGS: Sequences derived from NCBI's Whole Genome Shotgun database, with accession prefix provided; Whole Genome Sequencing of Short Reads: whole genomes were sequenced using short-read technologies. The methodologies varied for the species, and will be or have been published with other projects, so please contact the author(s) for information on the specific methodology and samples used (Xenarthrans, Proteles cristatus, Otocyon megalotis: Frédéric Delsuc, e-mail: Frederic.Delsuc@umontpellier.fr; Crocodylians: John Gatesy, e-mail: jgatesy@amnh.org; Dugong dugon: Mark Springer, e-mail: mark.springer@ucr.edu; SRA: sequences derived from NCBI's Sequence Read Archive; GenBank: sequences derived from NCBI's nucleotide collection; Bowhead Whale Genome Resource: sequences derived from http://www.bowhead-whale.org; Ensembl: sequences derived from Ensembl genome browser (www.ensembl.org)l; Discovar de novo: sequences derived genomes assembled via Discovar de novo  (<a href="https://software.broadinstitute.org/software/discovar/blog/">https://software.broadinstitute.org/software/discovar/blog/</a>). Coverage: indicates coverage of the whole genome (reported in NCBI or other source) or individual genes (derived from short read mapping). Scaffold and contig N50: reported in NCBI or other source. - Supplementary_Table_S2.xlsx: Accession numbers and functionality of AANAT in species examined. If Accession # indicated as “New”, sequence generated for this study and can be found in Supplementary Dataset S1. Parentheses after accession number indicates coordinates for sequence on the contig / scaffold. Exon colors code for the following: green = putatively functional; yellow = missing (e.g., negative BLAST results, negative mapping results); pink = one or more inactivating mutations found. Abbreviations for mutations are as follows: del = deletion; ins = insertion; start = start codon mutation; stop = premature stop codon; ? = ambiguity whether the mutation is shared among all members of the clade. Abbreviations in brackets following an inactivating mutation indicate shared inactivating mutation. Key for each abbreviation follows: Bacu = Balaenoptera acutorostrata; BALA = Balaenidae; BALAEN = Balaenopteridae; Bbon = Balaenoptera bonaerensis; CAB = Cabassous; Ccap = Cebus capucinus; CETA = Cetacea; CHLAM = Chlamyphoridae; CHOL = Choloepus; Cjac = Callithrix jacchus; CING = Cingulata; DASY = Dasypodidae; DELP = Delphinidae; DERM = Dermoptera; Erob = Eschrichtius robustus; INIA = Inia; FOLI = Folivora; GALE = Galeopterus; LIPO = Lipotes; Lobl = Lagenorhynchus obliquidens; MANI = Manidae; MONO = Monodontidae; MYRM = Myrmecophagidae; MYST = Mysticeti; NPP = Not present in Platanista or Physeteroidea, but present in other Odontocetes; NPZ = Not present in Ziphiidae, but present in other Odontocetes; Oorc = Orcinus orca; PEUT = Tolypeutinae; PHOC = Phocoenidae; PHOL = Pholidota; PHOR = Chlamyphorinae; PILO = Pilosa; PHYS = Physeteroidea; PONT = Pontoporia; Schi = Sousa chinensis; SIRE = Sirenia; Tadu = Tursiops aduncus; TOLY = Tolypeutes; VERM = Vermilingua; XEN = Xenarthra. - Supplementary_Table_S3.xlsx: Accession numbers and functionality of ASMT in species examined. See Table S2 caption for details. - Supplementary_Table_S4.xlsx: Accession numbers and functionality of MTNR1A in species examined. See Table S2 caption for details. - Supplementary_Table_S5.xlsx: Accession numbers and functionality of MTNR1B in species examined. See Table S2 caption for details. - Supplementary_Table_S6.xlsx: Codon frequency model selection. These are the results from one ratio dN/dS analyses using different codon frequency models. AIC = Akaike Information Criterion. - Supplementary_Table_S7.xlsx: Results of AANAT PAML dN/dS analyses for mammals. Model: BG = branch(es) grouped with background; fixed 1 = branch(es) fixed at 1. p’-value: p-value after Holm-Bonferroni correction for multiple testing. Model Comparison: if model comparison yields statistically significant differences (p < 0.05), model comparison bolded and given green background; if model comparison is still significant after Holm-Bonferroni correction, asterisk (*) added. For most models, w only shown for branch(es) of interest. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S1. - Supplementary_Table_S8.xlsx: Results of ASMT PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S2. - Supplementary_Table_S9.xlsx: Results of MTNR1A PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S3. - Supplementary_Table_S10.xlsx: Results of MTNR1B PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S4. - Supplementary_Table_S11.xlsx: Results of PAML analyses for sauropsids. - Supplementary_Table_S12.xlsx: Results of BLASTing and mapping short reads from Alligator mississippiensis RNA sequencing experiments. - Supplementary_Table_S13.xlsx: Supporting data for validating putative inactivating mutations. Validating data came from four general sources of information: mutations shared by more than one species within a clade, mutations shared by two sources of sequencing data for the same species, mutations validated by coverage of mapped short reads and statistically elevated dN/dS ratio estimates. For additional details, see Supplementary Tables S2–S5 and S7–S10, as well as Figure 2 and Supplementary Figures S8–S18. - Supplementary_Dataset_S1.txt: Genomic alignments in fasta format used to determine the pseudogene/functional status of all four melatonin genes in different taxonomic groups. - Supplementary_Dataset_S2.txt: Alignment of AANAT in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML.  - Supplementary_Dataset_S3.txt: Alignment of ASMT in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML.  - Supplementary_Dataset_S4.txt: Alignment of MTNR1A and MTNR1B in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML.  - Supplementary_Dataset_S5.txt: Codon alignments of AANAT used in selection pressure analyses with PAML.  - Supplementary_Dataset_S6.txt: Codon alignments of ASMT used in selection pressure analyses with PAML. - Supplementary_Dataset_S7.txt: Codon alignments of MTNR1A used in selection pressure analyses with PAML. - Supplementary_Dataset_S8.txt: Codon alignments of MTNR1B used in selection pressure analyses with PAML. - Supplementary_Dataset_S9.txt: Tree topologies in newick format used in selection pressure analyses with PAML.

opencc-by-4.0Jun 2021View details →

zenodo48/100

Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"

This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by Tuchen et al. (2022) published at Journal of Geophysical Research: Oceans. For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities. <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (Laurindo et al. 2017) that were horizontally interpolated to the resolution of the individual ship sections.

opencc-by-4.0Jan 2022View details →

zenodo48/100

Data for Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study - v0.1

Data repository for the manuscript titled: Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study. This repository includes all of the input data, processed data, and output data used for the project. This is a working version of our Tradeoff Analysis ready for interested users, peer reviewers, and others to run.  README file on GitHub: https://github.com/Laura-Geronimo/Geronimo-etal_2024_NaturalHazardsReview/blob/main/README.md

opencc-by-4.0Sep 2024View details →

zenodo48/100

Microphontes dichotomous, pathway key in SDD-format

Dichotomous, pathway key to species of Microphontes developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.

opencc-by-4.0Aug 2021View details →

zenodo48/100

Anasillomos dichotomous, pathway key in SDD-format

Dichotomous, pathway key to species of Anasillomos developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.

opencc-by-4.0Aug 2021View details →

zenodo48/100

A Curated Gene and Biological System Annotation of Adverse Outcome Pathways Related to Human Health

Adverse Outcome Pathways (AOPs) are multi-scale models of biological mechanisms connecting molecular initiating events to adverse outcomes through measurable key events. AOPs can guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation in chemical safety assessment. Here, we present a comprehensive molecular annotation of AOPs relevant to human health to embed the AOP framework into molecular data interpretation, which supports the development and application of novel AOP-based approaches in biomedical research. Please cite the following publication alongside this Zenodo entry when using the data: Saarimäki, L.A., Fratello, M., Pavel, A. et al. A curated gene and biological system annotation of adverse outcome pathways related to human health. Sci Data 10, 409 (2023). https://doi.org/10.1038/s41597-023-02321-w

opencc-by-4.0Oct 2022View details →

zenodo48/100

Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling (data for figures)

This is the data used to create the figures in the article "Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling". Published in EES Catalysis DOI: 10.1039/d3ey00122a Evan Johnson Etienne Boutin Shuo Liu Sophia Haussener Additional notes are given in the "ReadMe.txt" file.

opencc-by-4.0Jul 2023View details →

zenodo44/100

Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios

Recommended citation Article citation will be added once the article is available. Content <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#data-sources">Data sources</a></li> <li><a href="#changelog">Changelog</a></li> <li><a href="#references">References</a></li> </ul> Use of the dataset and full description Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section. The article will be referenced here as soon as it is published. Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset. When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below. Support If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de. Abstract This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data. Files included in the dataset The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows: <Source><Bunkers><Downscaling>.csv <Source> The "Source" flag indicates which input scenarios were used. <ul> <li>PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> <li>PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> </ul> <Bunkers> the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed. <Downscaling> The "Downscaling" flag indicates the downscaling technique used. <ul> <li>IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.</li> <li>IC: Regional emission intensity growth rates for all countries.</li> <li>CS: Constant emission shares as a reference case independent of the socio-economic scenario.</li> </ul> All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published. Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf. Notes If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data. Data format description (columns) "source" For PMRCP files source values are <ul> <li>RCPSSP<Bunkers><Downscaling>: unharmonized downscaled RCP SSP scenarios</li> <li>PMRCP<Bunkers><Downscaling>: downscaled RCP SSP scenarios harmonized to and combined with historical data</li> <li>PMRCPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> For PMSSP files source values are <ul> <li>SSPIAM<Bunkers><Downscaling>: unharmonized downscaled SSP IAM scenarios</li> <li>PMSSP<Bunkers><Downscaling>: downscaled SSP IAM scenarios harmonized to and combined with historical data</li> <li>PMSSPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> For possible values of <Bunkers> and <Downscaling> please see section <a href="#files-included-in-the-dataset">Files included in the dataset</a> above. "scenario" For PMRCP files the scenarios have the format <RCP><SSP><group>, where <ul> <li><RCP> denotes the RCP scenario. Values are RCP3PD, RCP45, RCP6, and RCP85.</li> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><groups> denotes the SSP basic elements GDP modeling group. Values are IIASA, OECD, and PIK. Not all RCP SSP combinations exist as some SSP storylines are not compatible with all RCP emissions scenarios. For details we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.</li> </ul> For PMSSP files the scenarios have the format <SSP><forcing><model> where <ul> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><forcing> denotes the radiative forcing level of the scenario. Values are 19, 26, 34, 45, 60, 85, and BL where 19 stands for 1.9W/m2 etc. and BL stands for baseline.</li> <li><model> denotes the Integrated Assessment Model (IAM) used to generate the scenario. Values can be found below</li> </ul> Model codes in scenario names <ul> <li>AIMCGE: AIM-CGE</li> <li>IMAGE: IMAGE</li> <li>GCAM4: GCAM</li> <li>MESGB: MESSAGE-GLOBIOM</li> <li>REMMP: REMIND-MAGPIE</li> <li>WITGB: WITCH-GLOBIOM</li> </ul> "country" ISO 3166 three-letter country codes or custom codes for groups: Additional "country" codes for country groups. <ul> <li>EARTH: Aggregated emissions for all countries</li> <li>ANNEXI: Annex I Parties to the UNFCCC</li> <li>NONANNEXI: Non-Annex I Parties to the UNFCCC</li> <li>AOSIS: Alliance of Small Island States</li> <li>BASIC: BASIC countries (Brazil, South Africa, India and China)</li> <li>EU28: European Union (still including the UK)</li> <li>LDC: Least Developed Countries</li> <li>UMBRELLA: Umbrella Group</li> </ul> "category" Category descriptions. <ul> <li>IPCM0EL: Emissions: National Total excluding LULUCF</li> <li>ECO: Economical data</li> <li>DEMOGR: Demographical data</li> </ul> "entity" Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4). Gases / gas baskets and underlying global warming potentials <ul> <li>CH4: Methane (CH4)</li> <li>CO2: Carbon Dioxide (CO2)</li> <li>N2O: Nitrous Oxide (N2O)</li> <li>FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF6, NF3</li> <li>FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF6, NF3</li> <li>KYOTOGHG: Kyoto greenhouse gases (SAR)</li> <li>KYOTOGHGAR4: Kyoto greenhouse gases (AR4)</li> </ul> "unit" The following units are used: <ul> <li>Million2011GKD: Million 2011 international dollars</li> <li>ThousandPers: Thousand persons</li> <li>kt: kilotonnes</li> <li>Mt: Megatonnes</li> <li>Gg: Gigagrams</li> <li>MtCO2eq: Megatonnes of CO2 equivalents using the GWPs defined by "entity"</li> <li>GgCO2eq: Gigagrams of CO2 equivalents using the GWPs defined by "entity"</li> </ul> Remaining columns Years from 1850-2100. Data Sources The following data sources were used during the generation of this dataset: Scenario data <ul> <li>RCP scenarios <a href="https://tntcat.iiasa.ac.at/RcpDb/">website/data</a></li> <li>SSP basic elements <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li>SSP IAM scenarios <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li>SSP CMIP6 scenarios <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> </ul> Historical data <ul> <li>CDIAC <a href="http://doi.org/10.3334/CDIAC/00001_V2017">data</a></li> <li>CEDS CMIP6 data <a href="https://www.geosci-model-dev.net/11/369/2018/">paper/data</a></li> <li>EDGAR version 4.3.2: <a href="http://doi.org/10.2904/JRC_DATASET_EDGAR">data</a>, <a href="https://doi.org/10.5194/essd-2017-79">paper</a></li> <li>IMO GHG report <a href="http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf">report</a></li> <li>PRIMAP-hist v2.1 <a href="http://www.earth-syst-sci-data.net/8/571/2016/">paper</a>, <a href="https://www.pik-potsdam.de/primap-live/primap-hist/">website</a>, <a href="https://doi.org/10.5880/PIK.2019.018">data</a></li> <li>PRIMAP-hist SocioEco v2.1 <a href="https://doi.org/10.5880/PIK.2019.019">data</a></li> </ul> Changelog For future versions References For full references we refer to the pdf version of the data description available in this repository and the list of related identifiers.

opencc-by-4.0Feb 2020View details →

zenodo44/100

Logical model for Molecular Pathways Enabling Tumour Cell Invasion and Migration

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.   Included files: <ul> <li>Master Model: the model includes detailed regulation of the major players involved in the crosstalks between Notch and p53 pathways</li> <li>Modular Model: the model is a reduction of the master model. To reduce the master model, we lumped together some entities that belonged to a module.</li> </ul>

opencc-by-4.0Nov 2015View details →

zenodo44/100

Data and R Code for 'Domestication via the commensal pathway in a fish-invertebrate mutualism'

This document contains all data and R code required to replicate the analyses in 'Domestication via the commensal pathway in a fish-invertebrate mechanism' as published in Nature Communications. The R Markdown provided includes descriptions of all variables and the code used for the analysis of the following eight datasets: 1. Transects 2. Census of farms 3. Paired choice experiments 4. Predation experiment 1 5. Predation experiment 2 6. Timed observations 7. Farm algae composition 8. Longfin damselfish body condition In addition, the R Markdown also includes descriptions of all variables for two additional datasets: 9. Estimates of mysid swarm density 10. Mysid waste excretion and nutrient availability A PDF version of the R Markdown with all output is also provided. Please see the methods section of the associated manuscript for further information on data collection and analysis procedures. Author contributions to data collection and analysis: RMB, JMC, ZLC, TLS & WEF collected the data; WEF, RMB, JMC, ZLC & AM implemented the analyses.  Correspond with: rohan.m.brooker@gmail.com

opencc-by-4.0Nov 2020View details →

zenodo44/100

Extracellular recordings from the locust, Schistocerca americana, olfactory pathway

This depository contains raw data from 14 experiments performed on adult locusts. The data are contained in HDF5 files (http://www.hdfgroup.org/HDF5/). They are stored as compressed integers coded on 16 bits as they came out of the A/D card. Recording details can be found in Pouzat, Mazor and Laurent (2002) Using noise signature to optimize spike-sorting and to assess neuronal classification quality. Journal of Neuroscience Methods 122: 43-57 (a pre-print version is available: http://xtof.perso.math.cnrs.fr/pdf/Pouzat+:2002.pdf). Each data file is subdivided in Groups corresponding the type of acquisition performed: one or several epochs of spontaneous activity recording; repetitive stimulation with a given odor. Each group is made of one (if say a single epoch of 60 seconds of spontaneous recording was made) or several (if say 100 stimulation with Citral were made) (sub-)groups containing the data of all the channels that were recorded during that epoch. Each of these sub-groups is made of 4 to 16 data sets: 1 dimensional arrays containing the raw data recorded from one of the 16 channels of our probe (made of 4 tetrodes) during a single acquisition epoch. All channels were sampled at 15 kHz. Each data file has attributes (metadata) README and LabBook. The first, README contains a shortened version of the present text; the second, LabBook contains a transcript of the lab book corresponding to the experiment. Most groups have a log_file_content attribute. This attribute contains a copy a text file that was automatically generated during data acquisition. Some recording details can be found there as well as the precise time and data of each recorded epoch. The data were kept for 14 years on CDs and about a third of the recordings got lost because of CD corruption! What's left still make 15 GBytes of data after compression: a substantial amount. This CD corruption explains why some groups don't have a log_file_content attribute: it was on a corrupted CD. Of the 14 experiments, 12 contain antennal lobe (the first olfactory relay of insects) recordings, 1 contains antennal lobe and alpha lobe recordings and 1 contains only alpha lobe recordings. Here the alpha lobe location should be taken with a little bit of caution since the latter is not as easy to locate as the antennal lobe in the locust. All data files start with the locust prefix, followed by the experiment year, month and date, like locust20000214.hdf5 an experiment performed on February 14 2000. Due to file size restriction on Zenodo, two experiments are split into two data files: locust20010124b_part1.hdf5 and locust20010124b_part2.hdf5 as well as locust20010214_part1.hdf5 and locust20010214_part2.hdf5. On two dates, two different experiments were performed: locust20010124a.hdf5 and locust20010124b_part1.hdf5 / locust20010124b_part2.hdf5 as well as locust20010208a.hdf5 and locust20010208b.hdf5. When a stimulation was applied, the following code is used: Odor name / Number of stimulation / Inter-stimulation interval / duration before the odor pulse / odor pulse duration / post pulse duration [odor dilution when several dilutions were used]. All times are in seconds. A very brief description at the group level of the files content follows (see the LabBook attribute of each individual file for details): <ol> <li>locust20000214.hdf5: <ul> <li>Citral / 70 / 30 / 3 / 0.5 / 6.5</li> <li>Cherry / 120 / 30 / 3 / 0.5 / 6.5</li> <li>Octaldehyde / 60 / 30 / 3 / 0.5 / 6.5</li> </ul> </li> <li>locust20000421.hdf5: <ul> <li>Spontaneous: 60 seconds of spontaneous activity</li> <li>1-Hexanol / 30 / 10 / 3 / 1 / 5.5</li> <li>Hexanal / 25 / 10 / 3 / 1 / 5.5</li> <li>Cis-3-hexen-1-ol / 25 / 10 / 3 / 1 / 5.5</li> <li>Trans-2-hexen-1-ol / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Hexen-3-ol / 25 / 10 / 3 / 1 / 5.5</li> <li>3-Pentanone / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Heptanol / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5</li> <li>2-Heptanone / 25 / 10 / 3 / 1 / 5.5</li> <li>3-Heptanone / 25 / 10 / 3 / 1 / 5.5</li> <li>Citral / 25 / 10 / 3 / 1 / 5.5</li> <li>Apple / 25 / 10 / 3 / 1 / 5.5</li> <li>Mint / 25 / 10 / 3 / 1 / 5.5</li> <li>Strawberry / 25 / 10 / 3 / 1 / 5.5</li> <li>Octaldehyde / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-5]</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-4]</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-3]</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-2]</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5 [10^-1]</li> <li>1-Octanol / 25 / 10 / 3 / 1 / 5.5 [1]</li> </ul> </li> <li>locust20000423.hdf5: <ul> <li>Spontaneous first: 60 seconds of spontaneous activity</li> <li>1-Hexanol / 25 / 10 / 3 / 1 / 5.5</li> <li>Hexanal / 25 / 10 / 3 / 1 / 5.5</li> <li>Cis-3-hexen-1-ol / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Hexen-3-ol / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Heptanol / 25 / 10 / 3 / 1 / 5.5</li> <li>2-Heptanone / 25 / 10 / 3 / 1 / 5.5</li> <li>3-Heptanone / 25 / 10 / 3 / 1 / 5.5</li> <li>Citral / 25 / 10 / 3 / 1 / 5.5</li> <li>Apple / 25 / 10 / 3 / 1 / 5.5</li> <li>Amyl Acetate / 25 / 10 / 3 / 1 / 5.5</li> <li>1-Hexanol / 25 / 10 / 3 / 1 / 5.5</li> <li>Spontaneous second: 60 seconds of spontaneous activity</li> </ul> </li> <li>locust20000613.hdf5: <ul> <li>Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1]</li> <li>Cis-3-hexen-1-ol / 10 / 30 / 3 / 1 / 16 [1/100]</li> <li>Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1/10]</li> <li>Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1]</li> <li>Cherry / 21 / 30 / 3 / 1 / 16</li> </ul> </li> <li>locust20000616.hdf5: <ul> <li>Spontaneous first: 60 seconds of spontaneous activity</li> <li>Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1]</li> <li>Spontaneous second: 60 seconds of spontaneous activity</li> <li>Spontaneous third: 60 seconds of spontaneous activity</li> <li>Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1/100]</li> <li>Cis-3-hexen-1-ol / 50 / 30 / 3 / 1 / 16 [1/10]</li> </ul> </li> <li>locust20000901.hdf5: <ul> <li>Vanilla / 5 / 30 / 3 / 1 / 16</li> <li>Spontaneous: 60 seconds of spontaneous activity</li> <li>Cherry / 30 / 30 / 3 / 1 / 16</li> <li>Spontaneous: 60 seconds of spontaneous activity</li> <li>Benzaldehyde / 30 / 30 / 3 / 1 / 16</li> <li>Spontaneous: 60 seconds of spontaneous activity</li> <li>Mint / 20 / 30 / 3 / 1 / 16</li> <li>Hexanal / 15 / 30 / 3 / 1 / 16</li> <li>Spontaneous: 60 seconds of spontaneous activity</li> <li>Cis-3-hexen-1-ol / 30 / 30 / 3 / 1 / 16</li> <li>Spontaneous: 60 seconds of spontaneous activity</li> <li>Trans-2-hexen-1-ol / 30 / 30 / 3 / 1 / 16</li> </ul> </li> <li>locust20010124a.hdf5: <ul> <li>Spontaneous: 2x29 seconds of spontaneous activity</li> <li>Spontaneous: 60x29 seconds of spontaneous activity</li> <li>Spontaneous: 80x29 seconds of spontaneous activity</li> </ul> </li> <li>locust20010124b_part1.hdf5 and locust20010124b_part2.hdf5: <ul> <li>Spontaneous: 60x29 seconds of spontaneous activity</li> <li>Spontaneous: 191x29 seconds of spontaneous activity</li> <li>Spontaneous: 59x29 seconds of spontaneous activity</li> </ul> </li> <li>locust20010131.hdf5: <ul> <li>Spontaneous: 90x29 seconds of spontaneous activity</li> <li>Spontaneous: 70x29 seconds of spontaneous activity</li> <li>Spontaneous: 5x59 seconds of spontaneous activity</li> <li>Spontaneous: 3x59 seconds of spontaneous activity</li> <li>Spontaneous: 3x59 seconds of spontaneous activity</li> <li>Spontaneous: 3x59 seconds of spontaneous activity</li> <li>Spontaneous: 3x59 seconds of spontaneous activity</li> <li>Spontaneous: 3x59 seconds of spontaneous activity</li> <li>Spontaneous: 3x59 seconds of spontaneous activity</li> <li>WithoutAntenna: 3x59 seconds of spontaneous activity (after antennal nerve cut)</li> <li>WithoutAntenna: 3x59 seconds of spontaneous activity (after antennal nerve cut)</li> <li>WithoutAntenna: 3x59 seconds of spontaneous activity (after antennal nerve cut)</li> </ul> </li> <li>locust20010201: <ul> <li>Continuous: 90x29 seconds of spontaneous activity</li> <li>Continuous: 20x29 seconds of spontaneous activity</li> <li>Citral / 50 / 30 / 3 / 1 / 25</li> <li>Citral / 50 / 30 / 10 / 1 / 18</li> <li>Citral / 50 / 30 / 10 / 1 / 18</li> <li>Continuous: 50x29 seconds of spontaneous activity</li> <li>Continuous: 45x29 seconds of spontaneous activity</li> </ul> </li> <li>locust20010208a.hdf5: <ul> <li>Spontaneous: 50x29 seconds of spontaneous activity</li> <li>Spontaneous: 80x29 seconds of spontaneous activity</li> </ul> </li> <li>locust20010208b.hdf5: <ul> <li>Spontaneous: 50x29 seconds of spontaneous activity</li> <li>Spontaneous: 50x29 seconds of spontaneous activity</li> <li>Citral / 50 / 30 / 10 / 1 / 18</li> <li>Citral / 120 / 30 / 10 / 1 / 18</li> <li>Citral / 50 / 30 / 10 / 1 / 18</li> <li>Citral / 25 / 30 / 10 / 1 / 18</li> </ul> </li> <li>locust20010214_part1.hdf5 and locust20010214_part2.hdf5: <ul> <li>Spontaneous: 30x29 seconds of spontaneous activity</li> <li>Spontaneous: 30x29 seconds of spontaneous activity</li> <li>Cis-3-hexen-1-ol / 25 / 30 / 10 / 1 / 18</li> <li>Citral / 25 / 30 / 10 / 1 / 18</li> <li>Vanilla / 25 / 30 / 10 / 1 / 18</li> <li>Octanol / 25 / 30 / 10 / 1 / 18</li> <li>Mint / 25 / 30 / 10 / 1 / 18</li> <li>Cis-3-hexen-1-ol / 25 / 30 / 10 / 1 / 18</li> <li>Spontaneous: 30x29 seconds of spontaneous activity</li> <li>Spontaneous: 30x29 seconds of spontaneous activity</li> <li>Cis-3-hexen-1-ol / 30 / 30 / 10 / 1 / 18</li> <li>Cis-3-hexen-1-ol / 11 / 30 / 10 / 1 / 18</li> <li>Cis-3-hexen-1-ol / 30 / 30 / 10 / 1 / 18</li> <li>Cis-3-hexen-1-ol / 30 / 30 / 10 / 1 / 18</li> </ul> </li> <li>locust20010217.hdf5: <ul> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> <li>Spontaneous: 2x29 seconds of spontaneous activity</li> <li>Spontaneous: 30x29 seconds of spontaneous activity</li> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> <li>Spontaneous: 10x29 seconds of spontaneous activity</li> </ul> </li> </ol>

opencc-zeroJul 2015View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

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DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record