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1,298 results for “Archiving”
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations
<p>This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations
<p>This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Global River Water Quality Archive (GRQA)
<p>A major problem related to large-scale water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both the availability and quality of open water quality data woould increase the potential to implement predictive modeling on a global scale. We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources: Canadian Environmental Sustainability Indicators program (CESI), Global Freshwater Quality Database (GEMStat), GLObal RIver Chemistry database (GLORICH), European Environment Agency (Waterbase) and USGS Water Quality Portal (WQP). The resulting dataset covering the timeframe 1898 - 2023 contains a total of over 17 million observations for 43 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset.</p> <p>GRQA <strong>data processing scripts</strong> are available at <a href="https://doi.org/10.5281/zenodo.5082147" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5082147</a>.</p> <p><strong>Last update: 2025-05-04</strong></p> <p><strong>Changes since GRQA_v1.3</strong></p> <p>The updated dataset now includes the observations from the latest versions of GEMStat and Waterbase. These additions extend the time series to 2023 in many sites, particularly in Europe.</p> <p>An overview of all the files in the dataset can be found in README_v1.4.md.</p> <p>Statistical overview of all 43 parameters is given in the data catalog file GRQA_data_catalog_v1.4.pdf.</p> <p>For more information about the development of this dataset look for Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, <a href="http://doi.org/10.5194/essd-13-5483-2021">https://doi.org/10.5194/essd-13-5483-2021</a>, 2021.</p>
Data archive: CICT for single cell RNA-seq network inference
<p>This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).</p><p>The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):</p><p>* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data<br>* CICT_v2/: CICT for experimental data<br>* CELLORACLEDB/: CellOracle for experimental data<br>* DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data<br>* DEEPDRIM72_v2/: DEEPDRIM for experimental data<br>* INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data<br>* INFERELATOR38_v2/: Inferelator-Prior for experimental data<br>* INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data<br>* INFERELATOR34_v2/: Inferelator-NoPrior for experimental data<br>* GENIE3/: GENIE3<br>* GRNBOOST2/: GRNBOST2<br>* LEAP/: LEAP<br>* PIDC/: PIDC<br>* PPCOR/: PPCOR<br>* SCENICDB/: SCENIC for experimental data<br>* SCNS/: SCNS<br>* SCODE/: SCODE<br>* SCRIBE/: SCRIBE<br>* SINCERITIES/: SINCERITIES<br>* SINGE/: SINGE<br>* RANDOM/: RANDOM</p><p>The methods were benchmarked against two kinds of scRNA-seq datasets:<br>* Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80. <br>* Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.<br> * Evaluation levels:<br> * L0: 500 highly varying genes plus TFs<br> * L1: 1000 highly varying genes plus TFs<br> * L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.<br> * Types of ground truths:<br> * Cell-type-specific ChIP-seq ground truth (L0, L1, L2)<br> * Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)<br> * Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)</p><p>The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).</p><p> </p>
Archive of Neptune (NSB) database backups
<p>This is the state of the Neptune (NSB) database (<a href="http://nsb.mfn-berlin.de">http://nsb.mfn-berlin.de</a>) as of 2023-06-05 archived by Johan Renaudie..</p><p>The database is given as a native PostgreSQL backup (<strong>nsb_postgresql.zip</strong>) and is also provided here in SQLite for convenience (<strong>nsb_sqlite.zip</strong>). As the foreign keys are not preserved in the latter, please check <a href="https://palaeo-electronica.org/content/2020/2966-the-nsb-database">Renaudie et al. 2020</a> to find back the table relations.</p><p>Denormalized tables are also provided in file <strong>denormalized_table.zip</strong>: </p><ul><li><strong>occurrences.csv</strong> contains the full unfiltered micropaleontological occurrences recorded in NSB.</li><li><strong>filtered_occurrences.csv</strong> represent a more typical output from the website, i. e. with possibly reworked specimens, open nomenclature taxa and questionable identifications filtered out.</li><li><strong>agemodels.csv</strong> finally contains the age models (as a serie of tiepoints) with their metadata</li></ul>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
Patristisches Textarchiv. Ein Open Access-Archiv antiker christlicher Texte
The "Patristic Text Archive" offers anyone interested a collection of texts and translations of Christian texts from antiquity (i.e. "Patristic" is conceived in a very broad sense).
UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph hash://md5/10663911550bb52a0f5741993f82db9d hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c
<p>A biodiversity dataset graph: UCSB-IZC</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.</p> <p>This dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].</p> <p>This archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].</p> <p>The images were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P ".*depict"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>And the occurrences were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P "occurrence/([0-9])+"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.</p> <p>To retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5660088/files/</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<br> <hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c> <http://purl.org/pav/previousVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c OK CONTENT_PRESENT_VALID_HASH 66438 hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c<br> hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 OK CONTENT_PRESENT_VALID_HASH 4093 hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844<br> hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef OK CONTENT_PRESENT_VALID_HASH 5746 hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef<br> hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b OK CONTENT_PRESENT_VALID_HASH 6147 hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston [2,3] v0.3.1. --<br> preston.jar</p> <p>-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a</p> <p>-- example image and meta-data --<br> sample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)<br> sample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .<br> [3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132<br> [4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c</p>
Words are monuments data-only archive
<p>These are the data that go with the paper <a href="https://besjournals.onlinelibrary.wiley.com/doi/full/10.1002/pan3.10302">"Words are monuments: Patterns in US national park place names perpetuate settler colonial mythologies including white supremacy"</a> (available 6 April 2022) by the above authors. Our code (and these data) are available at <a href="https://doi.org/10.5281/zenodo.5712009">https://doi.org/10.5281/zenodo.5712009</a>. This archive is here for folks who just want the data. This is the <strong>full dataset with name explanations</strong> and references for the explanations, traditional Indigenous place names for settler colonial place names (where available), additional categories for sorting, and more. </p> <p>Note: .csv files can be opened in Excel and Google Sheets.</p> <p>column info:</p> <p>A: Unique ID per row</p> <p>B: National Park place name is in</p> <p>C: Place name according to NPS visitor map</p> <p>D: Feature type (mountain, island, picnic area, etc.)</p> <p>E: Name type (person, non-human animal, plant, etc.)</p> <p>F: Natural or human constructed (human constructed includes so-called "ruins", as well as visitor centers, etc.)</p> <p>G: Is the word from an Indigenous or western language?</p> <p>H: Is it a traditional Indigenous place name?</p> <p>I: If it is Indigenous is it the name of an Indigenous person or people?</p> <p>J: Is it a translation of a traditional Indigenous PN?</p> <p>K: Word meaning class (similar to column E)</p> <p>L: Erasure -- see paper for definitions of each class below and decision trees</p> <ul> <li>Yes</li> <li>potentially</li> <li>no information</li> <li>not erasure - evidence it is a traditional Indigenous PN or settler built with western PN</li> <li>translation of traditional IPN)</li> </ul> <p>M: Dimensions of racism and colonialism -- see paper for definitions of each class below and decision trees</p> <ul> <li>No (traditional Indigenous PN, western built with western PN, or erasure as only problem)</li> <li>Name itself promotes racist ideas and/or violence against a group</li> <li>Named after person who supported racist ideas (but not physically violent)</li> <li>Named for a person who directly or use their power to indirectly perpetrate violence against a racial group</li> <li>Western use of Indigenous name (Appropriation)</li> <li>Other - truly does not fit any other classes</li> <li>No info - cannot find explanation</li> <li>Colonialism - memorializes colonialism</li> <li>Relevant western use of Indigenous name (i.e., appropriation of traditional name)</li> </ul> <p>N: Derogatory</p> <ul> <li>Yes</li> <li>Potentially</li> <li>No info</li> </ul> <p>O: explanation of name found in research</p> <p>P: Link to resources explaining name (for full citation for books cited, e.g., "name year" entries, see Table S1 in the paper).</p> <p>Q: Link to resources explaining name (if second link or source available)</p> <p>R: Indigenous name found in research</p> <p>v1.0.0 did not include columns O-R by mistake; corrected in this version update.</p>
MSCA-IF-896925_MaMo_WP1-A_Archives-Literature
<p>This dataset contains a set of data related to the archives' material and main Albanian published sources consulted during the implementation of the research activities foreseen in WP1. This dataset has been curated by Dr Federica Pompejano. The materials listed can be filtered according to the codes assigned to the five MaMo macro-areas of investigation and to the main issues identified as pivotal for the MaMo research project. The data contained in this dataset are open for public disposal under the terms and conditions described in the CC BY-NC-SA 4.0 license. The consultation of each archival or published source is possible at the archive or library that preserves the listed sources, under each library’s and/or archive's specific terms and conditions. Contacts of each archive and library are provided in the respective .xlsx files. This dataset and all its content are part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 896925.</p>
AIDA (Archive of Italian radiocarbon DAtes)
<p>The archive <strong>AIDA</strong> provides a collation of <strong>4,629</strong> radiocarbon dates from <strong>1,050</strong> archaeological sites in Italy from the Late Mesolithic until Late Antiquity (11 - 1.5 kya BP). These dates have been collected from existing online digital archives, and electronic and print original publications. </p> <p>List of versions:</p> <ul> <li><strong>5.0</strong> 9 April 2022 — 589 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>4.0</strong> 3 March 2022 — 35 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>3.0</strong> 13 January 2022 — Removal of some duplicates and 4 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>2.0</strong> 13 January 2022 — Removal of some duplicates and 4 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md').</li> <li><strong>1.0</strong> 3 August 2021 — First public release of the dataset on Zenodo</li> </ul>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
Data archive for Anaerobic methane oxidation in a coastal oxygen minimum zone: spatial and temporal dynamics
<p>Data collected during annual sampling campaigns to the coastal oxygen minimum zone of Golfo Dulce, carried out in January-February 2018, 2019 and 2020. Methods and results are presented and discussed in Steinsdóttir et al. 2022. Anaerobic methane oxidation in a coastal oxygen minimum zone: spatial and temporal dynamics. Environmental Microbiology, in press, doi: 10.1111/1462-2920.16003</p> <p>The content of files is as follows:</p> <p>nutrient_and_methane_concentrations.csv - Concentrations of methane, nitrite, nitrate, and ammonium.</p> <p>methane_oxidation_rates.csv - Rates of anaerobic methane oxidation.</p> <p>kinetics_of_anaerobic_methane_oxidation.csv - Kinetics of anaerobic methane oxidation, carried out in 2019.</p> <p>methylococcales.fa - Methylococcales 16S rRNA amplicon sequences</p> <p>methanofastidiosa.fa - Methanofastidiosa 16S rRNA amplicon sequences</p>
Pop-up satellite archival tagging data of Atlantic bluefin tuna in the Gulf of Lions, Northwestern Mediterranean Sea
<p>24 Atlantic bluefin tuna (Thunnus thynnus) individuals (117–158 cm fork length) were tagged with pop-up archival tags in the Gulf of Lion, NW-Mediterranean Sea between 2015 and 2016.</p> <p><strong>Tag programming and data</strong></p> <p>The tags applied (miniPATs by Wildlife Computers, https://wildlifecomputers.com) can record depth and temperature time series (denoted hereafter as DepthTS and TempTS, respectively) at a temporal resolution of 3–5 s (depending on the predefined deployment duration) and a vertical resolution of 0.5 m. Based on these data, the tag calculates and stores additional data products such as PAT-style Depth–Temperature profiles (PDT), time at depth data, and time at temperature. After pop-up, the tags transmit user-defined data products and subsets from the recorded data sets. All our tags were configured to transmit the following data products: daily light curves, DepthTS, and PDT. In order to maximize data coverage of the transmitted datasets, we decreased the temporal resolution of the DepthTS and PDT data after the first tagging campaign in 2015 from 150 to 600 s and 6 to 24 h, respectively. For both years, deployment durations were set to 150 and 90 d during spring (April–May) and summer (August–September), respectively. A description of the electronic tagging procedure can be found in <a href="https://doi.org/10.1093/icesjms/fsaa083">Bauer et al. (2020)</a>.</p> <p>Seven tags were physically recovered, providing the complete archived time series data at a resolution of 3–5 s. Nineteen tags provided more than 7 d of complete DepthTS data (i.e. without transmission gaps). Three tags from 2016 had deployment durations of <1 week (#15P0983, #15P0985, and #15P0986) because of hardware failure.</p> <p>Provided files contain raw tag data (transmitted and recovered datasets) from the Wildlife Computers Data Portal as well as related GPE3 model runs (geolocation estimates).</p> <p>We thank the crews of the Cyngali and Roussillon Fishing recreational fishing vessels for their cooperation during the tagging cruises. This tagging study was part of the BLUEMED project and funded by the French National Research Agency (ANR; Project-ID ANR-14-ACHN-0002).</p> <p> </p>
Ethiopian archives of nature: photographs
<p>01. Hiking trail between Sankaber and Gich, Simien, 2013.</p> <p>02. The material shaping of nature: Village of Gich, Simien Mountains, September 2013 and January 2019.</p> <p>03. An archival collection in the environmental history of Ethiopia: EWCA warehouse, Lideta district, July 2016; EWCA offices, Yobek district, April 2021. (Photographs by Guillaume Blanc [2016] and Kidanemariam Woldegiorgis Ayalew [2021]).</p> <p>04. The “John Blower” collection: “JB” binders, Ethiopian Wildlife Conservation Authority Library, Addis Ababa, 2016.</p>
Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"
<p>Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"</p> <p>Containing raw stable isotope and trace element data used in the study</p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Webis-Web-Archive-Quality-22
<p>Dataset accompanying the TPDL'22 publication "<a href="https://webis.de/publications.html?q=Visual+Web+Archive+Quality+Assessment">Visual Web Archive Quality Assessment</a>" of Theresa Elstner, Johannes Kiesel, Lars Meyer, Max Martius, Sebastian Schmidt, Benno Stein, and Martin Potthast.</p>
Global ice drilling and archive location data for select ice cores
<p>This document includes ice drill site information and ice core repository information for select ice cores retrieved between 1958 and 2022. Included data are not representative of all ice cores drilled during this time period, nor are they representative of all ice core samples collected and maintained by all of the contributing programs and facilities. Data are presented as they were provided by contributing facilities in 2022, when they were used to generate a figure for an article in Past Global Changes Magazine (doi.org/10.22498/pages.30.2.98).</p> <p>The data describe ice core drilling sites (latitude, longitude, elevation, site name), ice core samples (bottom depth, bottom age, core diameter, core completion date, corresponding publications), and ice core storage facilities (latitude, longitude, name).</p> <p>Contributing facilities include the following: Alfred Wegener Institute (Germany), Australian Antarctic Division (Australia), Australian Antarctic Program Partnership (Australia), Byrd Polar Center - University of Ohio (United States of America), Canadian Ice Core Lab (Canada), Chiba University (Japan), Commonwealth Scientific and Industrial Research Organization (Australia), Institute of Environmental Geosciences - University of Grenoble (France), Institute of Low Temperature Science - University of Hokkaido (Japan), Institute of Polar Science and Engineering - Jilin University (China), Karakoram International University (Pakistan), Lanzhou Institute of Glaciology and Geocryology (China), Nagoya University (Japan), National Institute of Polar Research (Japan), National Science Foundation Ice Core Facility (United States of America), New Zealand National Ice Core Facility (New Zealand, Physics of Ice Climate and Earth - University of Copenhagen (Denmark), Polar Research Institute of China (China), Research Institute for Humanity and Nature (Japan), and Tibet University. </p> <p>We are grateful to each of these facilities for contributing details of their ice core collections for this work. </p> <p> </p> <p> </p> <p>Electronic data accessibility and sample request procedures for a few of these facilities of which the authors are aware are listed below.</p> <p>Australia: data can be obtained from the Australian Antarctic Data Centre (<a href="https://urldefense.com/v3/__https://data.aad.gov.au/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnumedvhR$">https://data.aad.gov.au</a>); access to ice from the Australian Antarctic Program is via application (see <a href="https://urldefense.com/v3/__https://www.antarctica.gov.au/science/information-for-scientists/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnosG8VPm$">https://www.antarctica.gov.au/science/information-for-scientists/)</a></p> <p>Denmark: data can be obtained from <a href="https://www.iceandclimate.nbi.ku.dk/data/">www.iceandclimate.nbi.ku.dk/data</a>; the ice sampling request procedure is listed here: <a href="https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/">https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/</a> </p> <p>United States: many ice core datasets can be found at the NOAA World Data Center (<a href="https://www.ncei.noaa.gov/products/paleoclimatology/ice-core">https://www.ncei.noaa.gov/products/paleoclimatology/ice-core</a>); the allocation policy for ice core samples can be found here: <a href="https://icecores.org/policy">https://icecores.org/policy</a>.</p>
Samoan Passage Bathymetry Data Archive
<p>Samoan Passage Bathymetry Data Archive. Please note that the copy here on zenodo contains only the GitHub repository, data are stored elsewhere.</p> <p>Head to <a href="https://github.com/gunnarvoet/sp-data-archive-bathy">https://github.com/gunnarvoet/sp-data-archive-bathy</a> for instructions on how to clone the full dataset or download data files manually at <a href="https://osf.io/7anhw/">https://osf.io/7anhw/</a>.</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.