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zenodo44/100

MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"

Corresponding peer-reviewed publication This dataset corresponds to all input and output files that were used in the study reported in: <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Summary The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. We aim to answer two questions: 1.     What is the intrinsic spatial resolution of global river dynamics? 2.     How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes? Data sources The following sources were used to produce files in this dataset: <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> Software The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling. Data Products The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii). Riv_coast.zip contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean. ·       riv_coast.zip     o      cor: riv_coast_pfaf_ii_COR.shp     o      uncor: riv_coast_pfaf_ii_UNCOR.shp   Qout_rivwidth.zip contains csv files of the aggregate river discharge to the ocean (km3/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       Qout_rivwidth.zip: Qout_pfaf_ii_rivwidth.csv   V_rivwidth_low.zip contains csv files of the aggregate river storage (km3) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_low.zip: V_pfaf_ii_rivwidth_low.csv   V_rivwidth_nrm.zip contains csv files of the aggregate river storage (km3) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_nrm.zip: V_pfaf_ii_rivwidth_nrm.csv   V_rivwidth_hig.zip contains csv files of the aggregate river storage (km3) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_hig.zip: V_pfaf_ii_rivwidth_hig.csv   Largest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins. ·       largest_rivs.zip     o      cat: cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins     o      csv: Q_df_top10.csv – total discharge contributed by each basin     o      riv: riv_top10_nxx.shp – river reaches that drain the 10 largest basins   Smallest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m. ·       smallest_rivs.zip     o      cat: cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii     o      csv: Q_df_top10.csv – total discharge to the ocean from each narrow river reach     o      riv: riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii   Global_summary.zip contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage. ·       global_summary.zip     o      Qout_rivwidth: global summary files for discharge to the ocean (km3/yr) under river width sampling     o      V_rivwidth_low: global summary files for total river storage (km3) for the low residence time scenario under river width sampling     o      V_rivwidth_nrm: global summary files for total river storage (km3) for the normal (medium) residence time scenario under river width sampling     o      V_rivwidth_hig: global summary files for total river storage (km3) for the hig residence time scenario under river width sampling     o      cat_small_gl: cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean   Rivwidth_sens.zip contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations. ·       Rivwidth_sens.zip     o      riv_coast     o      Qout_rivwidth_VIC     o      Qout_rivwidth_CLSM     o      Qout_rivwidth_NOAH     o      V_rivwidth_low_VIC     o      V_rivwidth_nrm_VIC     o      V_rivwidth_hig_VIC     o      V_rivwidth_low_CLSM     o      V_rivwidth_nrm_CLSM     o      V_rivwidth_hig_CLSM     o      V_rivwidth_low_NOAH     o      V_rivwidth_nrm_NOAH     o      V_rivwidth_hig_NOAH     o      global_summary_VIC     o      global_summary_CLSM     o      global_summary_NOAH   Cor_sens.zip contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis. ·       Cor_sens.zip     o      Qout_rivwidth_ENS     o      V_rivwidth_low_ENS     o      V_rivwidth_nrm_ENS     o      V_rivwidth_hig_ENS     o      global_summary_ENS   Width_val.zip contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018). ·       Width_val.zip: width_validation_pfaf_ii.csv   Known bugs in this dataset or the associated manuscript No bugs have been identified at this time.   References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588. https://doi.org/10.1126/science.aat0636 Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-024-01421-5 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. https://doi.org/10.1029/2019WR025287 Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). Bulletin of the American Meteorological Society, 102(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

opencc-by-sa-4.0Sep 2024View details →

zenodo44/100

FASTA file containing the MYB encoding gene An2-like and Ant1 coding sequences corresponding to wild and cultivated tomato accessions

The coding sequence (CDS) of the MYB encoding genes Ant1 and An2-like. Sequences were retrieved from regions corresponding to the Aft locus from Solanum galapagense accession LA1141, S. lycopersicum variety OH8245, and  84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences were compared to available CDS available from the Sol genomics network (SGN) and the National Center for Biotechnology Information. The CDS was retrieved from S. lycopersicum variety Indigo Rose [MN433087 (Yan et al., 2020)], S. lycopersicum accession LA1996 [MN242011.1, EF433417.1( Sapir et al., 2008; Colanero et al., 2020)], and S. chilense accession LA1930 [MN242012.1 (Colanero et al., 2020)], The orthologous CDS corresponding to the Aft MYB encoding genes from Solanum tuberosum L. Group Phureja clone DM1-3 genome (PGSC DM v4.03 Pseudomolecules) was retrieved from the Potato Genome Sequence Consortium (PGSC: Potato Genome Sequencing Consortium et al., 2011), and the Capsicum annum cv. CM334 genome was retrieved from Capsicum annuum cv CM334 genome chromosome release 1.55 (Hulse-Kemp et al. 2018). These CDS were obtained using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at https://solgenomics.net/tools/blast/). Comparison of syntenic chromosomal regions using known positions of tomato, potato, and pepper markers with comparative map viewer from  SGN: (available at https://solgenomics.net/cview) on chromosome 10, was used as a quality check for S. tuberosom and C. annuum. Orthologous CDS corresponding to Salvia miltiorrhiza, Arabidopsis thaliana, [NM_105308.2, NM_105310.4 (Teng et al., 2005, Cominelli et al., 2008; Beradini et al., 2015)] were chosen based on tomato Aft sequence homology and gene annotations of positive R2R3 MYB regulation of anthocyanin. The CDS corresponding to the Aft genes were retrieved from the CDS reference genomes available from the Sol Genomics Network SGN: Tomato Genome CDS (ITAG release 4.0), Potato PGSC DM v3.4 CDS sequences, Capsicum annuum cv CM334 Genome CDS (release 1.55), or from the National Center for Biotechnology Information (NCBI: https://www.ncbi.nlm.nih.gov) reference sequences (RefSeq) section of the Genbank records. When accessed from Genank records, the CDS sequence was extracted from the “features” section and exported as a FASTA file.

opencc-by-4.0Nov 2021View details →

zenodo44/100

S'écrire au XIXe siècle. Une correspondance familiale

S'écrire au XIXe siècle. Une correspondance familiale This dataset contains a digital edition of the correspondace of the Merzdorff-Duméril-Froissart family from the late XVIIIth century up to approx. 1920, published on the website <a href="https://lettresfamiliales.ehess.fr">lettresfamiliales.ehess.fr</a>. The data is available under the Etalab 2.0 open licence, with the exception of a small set of images from the National Museum of Natural History of France (see section "Credits and license"). If you re-use a document or the entire corpus, please credit its authors using the citation template in section "How to cite this dataset". About this dataset This dataset presents an updated and enriched version of the Merzdorff-Duméril-Froissart family's letters. This family has textile manufacturers, scientists from the French National Museum of Natural History and high ranking civil and military officials that elevate their social status from the late XVIIIth century to the early XXth century. This correspondence was an instrument of social promotion, used by actors to increase their social status and keep memory of this elevation in the XIXth century French society. Carried out by a team of engineers from the Centre de Recherches Historiques (UMR 8558), the digital edition of the corpus resulted in the publication of more than 3,000 letters and approx. 2,000 additional documents, published on <a href="https://lettresfamiliales.ehess.fr">lettresfamiliales.ehess.fr</a>. as a semantik wiki. The dataset contains an export of all the documents published on the wiki, grouped in four categories and available in data.zip. As of january 2022: <ul> <li>3248 letters</li> <li> 1002 biographies</li> <li>110 monographies</li> <li>53 various family papers</li> </ul>   Indexes and metadata Each category is associated with a CSV file which provide the names and local paths of the documents for this category, along with a set of semantic metadata attached to every page of the corpus. The following table provide details about the metadata you can find in the indexes.   <table> <thead> <tr> <th scope="col">Metadata</th> <th scope="col">English translation</th> <th scope="col">Description</th> <th scope="col">Category</th> </tr> </thead> <tbody> <tr> <td>Date de rédaction</td> <td>Writing date</td> <td>The date where this letter was written, as inscribed on the original document.</td> <td>Letters</td> </tr> <tr> <td>Signataire</td> <td>Signatory</td> <td>The author of the letter.</td> <td>Letters</td> </tr> <tr> <td>Destinataire</td> <td>Recipient</td> <td>Recipient of the letter.</td> <td>Letters</td> </tr> <tr> <td>Lieux d'expédition et de réception</td> <td>Place of dispatch and receipt</td> <td>From where the letter was sent, and where it was received.</td> <td>Letters</td> </tr> <tr> <td>Sous-titre</td> <td>Subtitle</td> <td>The letter subtitle.</td> <td>Letters</td> </tr> <tr> <td>Titre</td> <td>Title</td> <td>The document / wiki page title.</td> <td>All</td> </tr> <tr> <td>Mentionne</td> <td>Mentions</td> <td>A person cited or mentioned, even indirectly.</td> <td>All</td> </tr> </tbody> </table> A few other information are given with each document : <ul> <li>displaytitle: the title displayed on the wiki can be different from the actual page name.</li> <li>exists: 1 if the page is not deleted</li> <li>fullurl: the actual URL of the document on lettresfamiliales.ehess.fr</li> <li>namespace: the page namespace. Should be always 0.</li> <li>path: the local path to the files of this document.</li> </ul>   Texts and images Documents are stored in separate folders. Letters are grouped by years and decade, the other categories by alphabetical order. Images attached to documents are stored in the same folder. All documents are available in four formats: <ul> <li>HTML as rendered from the wiki, with internal hyperlinks converted to local links.</li> <li>PDF</li> <li>raw text containing only the subtitle and trancription for letters, and the main content for the other pages.</li> <li>XML-TEI</li> </ul>   How to cite this dataset To reference the entire dataset, use the citation generated by the repository. To cite a particular document within the dataset you can use this template: "{document Title}, {document subtitle if any}". In "S’écrire au XIXème siècle. Une correspondance familiale.". Danièle Poublan. École des Hautes Études en Sciences Sociales (EHESS), Centre de Recherches Historiques.  {Date}, {Dataset Version}, {DOI}. Credits and license The fac-simile displayed in the documents listed below come from original documents stored at the National Museum of Natural History of France. All rights reserved. All texts and other images were created by Cécile Dauphin and Danièle Poublan and are published under Open License 2.0 (see LICENSE.txt). You are therefore free to copy, reuse, modify or republish the data, provided you credit the authors. Images from the National Museum of Natural History of France: - Mardi 6 mai 1856 (Ms 2600, n°1044) ; - Lundi 3 septembre 1860 (Ms 2739, n°179) ; - Samedi 7 mai 1853 (Ms 2600, n°1048) ; - Jeudi 6 mai 1852 (Ms 2745, n°595) ; - Mercredi 23 avril 1828 (Ms 1974, n°588) ; - Dimanche 8 juillet 1860 (Ms 2754, n°78) ; - Samedi 20 août 1853 (Ms 2745, n°596) ; - Fin août ou début septembre 1844 (Ms 2745, n°591) ; - Lundi 9 mai 1842 (Ms 2745, n°594) ; - Dimanche 23 octobre 1814 (Ms 2716-1, pièces 17 et 18) ; - Vendredi 14 mars 1800, 27 ventôse an VIII (Ms 1975 (1), n°794) ; - Vendredi 5 novembre 1847 (Ms 2745, n°593) ; - Dimanche 8 janvier 1854 (Ms 2600, n°1050) ; - Jeudi 3 janvier 1833 (Ms 1965, n°241) ; - 1816 - Rapport à l’Académie, au nom de la section, sur André Marie Constant Duméril (Ms 2716-1, pièce 5) ; - Lundi 9 avril 1855 (Ms 2600, n°1051) ; - Fin août 1860 (Ms 2739, n°178) ; - Lundi 24 juillet 1843 (Ms 2745, n°590) ; - Dimanche 18 mai 1856 (Ms 2600, n°1045) ; - 1853 (Ms 2745, n°597) ; - Mardi 14 décembre 1845 (Ms 2745, n°592) ; - Mercredi 29 avril 1812 (Ms 2528, n°56) ; - Lundi 4 Juin 1860 (B (Ms 2739, n°177) ) ; - 1852 – Faire-part de décès d’Alphonsine Delaroche, épouse d’André Marie Constant Duméril (Ms 2600, folio°1049) ; - Samedi 6 décembre 1834 (Ms 2600, pièce 1047) ; - Samedi 7 février 1835 (Ms 1986, n°478) ; - Jeudi 27 octobre 1825 (Ms 1997, n°132) ; - Dimanche 14 septembre 1834 (Ms 2600, pièce 1046) ; - Mercredi 24 novembre 1832 (Ms 638, n°187) ;

opencc-by-2.0Jan 2022View details →

zenodo44/100

AMS and FTIR measurements and the corresponding codes for their statistical combination

This dataset includes the post-processed FTIR and AMS data for the particulate phase obtained by Yazdani et al., https://doi.org/10.5194/amt-2021-186 form wood and coal burning experiments in the PSI environmental simulation chamber. It also contains the codes for the statistical combination of AMS and FTIR measurements to estimate the high-time-resolution functional group composition of organic aerosols.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Reflectometry curves (XRR and NR) and corresponding fits for machine learning

This is a compiled dataset of raw X-ray reflectivity (XRR, reflectometry) measurements together with corresponding fit parameters, intentionally published to use as training or test data for machine learning models. (The authors aim to include NR data in further versions of this dataset and plan to include other substrates and materials for XRR. Contributions welcome!) An interactive documentation can be found in "README.html" or at <a href="https://schreiber-lab.github.io/reflectometry-dataset">https://schreiber-lab.github.io/reflectometry-dataset</a>. <ul> <li>Data structure</li> </ul> All data is provided in an hdf5 file, following <a href="https://www.nexusformat.org/">NeXus</a> convention with respect to the provided metadata in the hdf5 attributes. Some datesets have been measured in-situ and therefore there are stacks of curves that correspond to the different layer thicknesses of the same material on top of SiOx. The measured data is provided under experimental and the corresponding fit parameters under fit. Additional information is collected in metadata. <ul> <li>Where to find the dataset and how to contribute</li> </ul> Have a look at <a href="https://github.com/schreiber-lab/reflectometry-dataset">github</a> and <a href="https://doi.org/10.5281/zenodo.6497438">zenodo</a>. In case you wish to contribute further curves to this dataset or have ideas how to improve the dataset or where else to deposit it, please contact the authors at softmatter AT ifap.uni-tuebingen.de.

opencc-by-4.0May 2022View details →

zenodo44/100

RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"

Corresponding peer-reviewed publication This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in: <ul> <li> Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1. </li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.  Known bugs and limitations in this dataset or the associated manuscript. In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate.  The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location.  As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect.  This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented.  The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name.  Correct figures can also be created using corresponding file names that were not so appended.  Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper.  The slope of the daily discharge STDE trend initially reported as \(\alpha = 0.3876\) in Figure 5c changes to \(\alpha = 0.4507\) after correction.  The resulting value of the ideal inflation factor hence changes from \(I = {1 \over 0.3876} \approx 2.58\) to \(I = {1 \over 0.4507} \approx 2.22\). This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to \(I = 2.58\) than to \(I = 1\) or \(I = 5\), i.e. the three values that were evaluated. Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended.

opencc-by-4.0Mar 2020View details →

zenodo44/100

Diffuse reflectance spectra of coated plates and corresponding plots transformed Kubelka-Munk function versus the energy of light (eV)

The link contains UV-DRS  results of  TiO2/Fe2O3 layered composites (from commercial nanoparticles) and corresponding bandgap energies

opencc-by-4.0Sep 2022View details →

zenodo44/100

Corresponding Dataset for "Ganymede's Ionosphere observed by a Dual-Frequency Radio Occultation with Juno"

Corresponding Dataset for "Ganymede’s Ionosphere observed               by a Dual-Frequency Radio Occultation with Juno"                              README FILE                               VERSION 2                             Dustin Buccino                              April 22, 2024                        Jet Propulsion Laboratory                   California Institute of Technology ============================================================================= VERSION 2 INFORMATION =============================================================================    Version 2 of this dataset separates the Electron Density profile from the main data files and makes a correction to the egress profile that was discovered. Differences in egress profile are very small and within the uncertainties. Furthermore egress is statistically a non-detection (zero densities), but for sake of accuracy they are reposted to be consistent with the publication. ============================================================================= INTRODUCTION =============================================================================     This dataset contains processed radio science data and results of the Juno Ganymede radio occultation. This dataset is provided in order to  supplement the submitted article to the "Geophysical Research Letters" journal:     Buccino, D.R., et al (2022), Ganymede’s Ionosphere observed by a      Dual-Frequency Radio Occultation with Juno, Geophysical Research      Letters, submitted February 2022.     Please note the raw data used in this analysis are not provided in this supplementary dataset. The raw Juno Gravity Science Data may be found at  the Planetary Data System:     Buccino, D. R. (2016). Juno jupiter gravity science raw data set      V1.0, JUNO-J-RSS-1 JUGR-V1.0, NASA planetary data system (PDS).      Retrieved from https://atmos.nmsu.edu/PDS/data/jnogrv_1001/      ============================================================================= ARCHIVE INFORMATION =============================================================================     This archive contains two files within the root directory.          ROOT      `- JunoG34OccData_Egress_v2.csv             This data file contains the EGRESS data relevant to the radio             occultation. The data is a timeseries of impact parameter, sky             sky frequency at X-band and Ka-band, the dual-frequency              combination, the calibrated dual-frequency, Total Electron              Content.      `- JunoG34_GRL_Egress_Profile_v2.csv             This data file contains the EGRESS Electron density, and             1-sigma electron density uncertainty.      `- JunoG34OccData_Ingress_v2.csv             This data file contains the INGRESS data relevant to the radio             occultation. The data is a timeseries of impact parameter, sky             sky frequency at X-band and Ka-band, the dual-frequency              combination, the calibrated dual-frequency, Total Electron              Content.      `- JunoG34_GRL_Ingress_Profile_v2.csv             This data file contains the INGRESS Electron density, and             1-sigma electron density uncertainty. ============================================================================= FILE FORMAT =============================================================================     This dataset contains only a comma-separated text files which are given with the "*.csv" extension.     CSV FILES     -------------------------------------------------------------------------     The Comma-Separated Value (CSV) files are plain-text files. Values in     each data file are separated using a comma ",". Each column is defined      by a header row which provides a description of each column.      ============================================================================= ACKNOWLEDGMENTS ============================================================================= This work was carried out at the Jet Propulsion Laboratory,  California Institute of Technology, under contract with the National  Aeronautics and Space Administration. Government sponsorship acknowledged. EG, LGC, PT, MZ and AC are grateful to the Italian Space Agency (ASI) for  financial support through Agreement No. 2018-25-HH.0 in the context of ESA's  JUICE mission, and Agreement No. 2017-40-H.1-2020, and its extension  2017-40-H.02020-13-HH.0, for ESA’s BepiColombo and NASAs Juno radio science  experiments. EG is grateful to "Fondazione Cassa dei Risparmi di Forlì" for  financial support of his PhD fellowship. PS and AH were supported by NASA Contract NNM06AA75C from the Marshall  Space Flight Center under subcontract 699054X from Southwest Research  Institute. ============================================================================= PRIMARY POINT OF CONTACT ============================================================================= Dustin Buccino Jet Propulsion Laboratory Planetary Radar and Radio Sciences (818) 393 - 1072 Dustin.R.Buccino@jpl.nasa.gov ============================================================================= ACRONYMS AND ABBREVIATIONS =============================================================================      ASCII  American Standard Code for Information Interchange      DOY    Day of year      DSN    Deep Space Network      JPL    Jet Propulsion Laboratory      NAIF   Navigation Ancillary Information Facility      NASA   National Aeronautics and Space Administration      PDS    Planetary Data System      RS     Radio Science      RSS    Radio Science Subsystem      SIS    Software Interface Specification      TXT    Text file      UTC    Universal Time, Coordinated

opencc-by-3.0-usFeb 2022View details →

zenodo44/100

Dataset for KIOS CoE Sandboxing use-case SUC4 corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme (IEC 61850 GOOSE)

The datasets reflect on two main scenarios (S1-S2) related to SUC4 - corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme. The first scenario explores the response of the coordinated overcurrent protection when circuit breakers (CBs) are healthy, under normal operation, i.e., SUC4/S1(without attack), and the under a FDI cyberattack on IEC 61850 - GOOSE communication protocol, i.e., SUC4/S1(with FDI attack). Similarly, the second scenario investigates the response of the coordinated overcurrent protection when there a mechanical failure in the CB of the downstream feeder, under normal operation, i.e., SUC4/S2(without attack), and the under a message suppresion (MS) cyber-attack on GOOSE protocol, i.e., SUC4/S2(with MS attack). Details regarding the datasets captured during the execution of each scenario (with and without attacks), including electrical measurements and network traffic, are briefly rsummarized below, while the full details are provided in the supporting documents. <ul> <li>SUC4/S1(without attack) datasets/Normal operation (without cyber-attack on GOOSE) when CBs are healthy : This dataset is related to the operation of the sandboxing use case SUC4 described in this document, which examines operation of the protection scheme in a substation using overcurrent protective relays (IEDs) in the sandboxing environment, that communicate with each other via IEC6180/GOOSE protocol. Specifically, this dataset corresponds to the first scenario (S1) of SUC4, without any attack. More details about the scenario related to this dataset can be found in Section 1.3.1 of the SUC4 supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.</li> <li>SUC4/S1(with FDI attack) datasets/FDI cyber-attack on GOOSE signals when CBs are healthy:  This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database. </li> <li>SUC4/S2(without attack) datasets/ Normal operation (without attack on GOOSE) when CB presents a failure: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database. </li> <li>SUC4/S2(with MS attack) datasets/MS cyber-attack on GOOSE signals when CB presents a failure: This dataset corresponds to the second scenario (S2) of SUC4, where an MS cyber-attack is conducted in the local network in order prevent critical benign messages, such inter-trip messages requesting backup protection, to reach their destination (back-up IED) when a CB failure occurs during a short-circuit event. As a result, the duration of a short-circuit is prolonged or the protection scheme is not able to clear the short-circuit event, which can cause catastrophic failures to power system. More details about the scenario related to this dataset can be found in Section 1.3.2 of the support document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database. </li> </ul>

opencc-by-4.0Jul 2024View details →

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Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids

These datasets  illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section 1.3 of the supporting document of SUC5 (accompanied by an in-depth analysis of the processes and an impact assessment). All data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals.  <ul> <li>SUC5/S1 datasets/MITM with FDI cyber-attack in an active distribution grid (grid-connected): This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution.   </li> <li>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode): This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution.   </li> </ul>

opencc-by-4.0Jul 2024View details →

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Corresponding spreadsheet to the Paper 'Variability in the assessment of childcare in 30 European countries'

The spreadsheet provides the list of indicators reported by the national experts to assess the quality of child care in the relevant countries along with those gathered from official documents provided by the experts. It has been adopted to the Paper 'Variability in the assessment of childcare in 30 European countries'.

opencc-by-4.0Jul 2019View details →

zenodo44/100

GRTSmh_diffres: the raster data source GRTSmaster_habitats converted to 9 hierarchical cell address levels at the corresponding lower resolution

The <code>GRTSmh_diffres</code> data source file is a file collection, composed of nine monolayered GeoTIFF files of the <code>INT4S</code> datatype plus a GeoPackage with six polygon layers: <ul> <li> The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the <code>GRTSmh_brick</code> data source (<a href="https://doi.org/10.5281/zenodo.3354403">link</a>). This means that they provide the decimal (i.e. base 10) integer values of these higher hierarchical levels of the GRTS cell addresses of the raw data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-<code>NA</code> cells of the original <code>GRTSmaster_habitats</code> raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf5">source code</a> to do such things. </li> <li> The GeoTIFF files provide the respective levels 1 to 9 of the <code>GRTSmh_brick</code> data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of <code>NA</code> cells around Flanders at level 0 implies that, with decreasing resolution, the raster's extent increases and larger areas outside Flanders are covered by non-<code>NA</code> cells along the border. </li> </ul> The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_30_GRTSmh_diffres">'n2khab-preprocessing' at commit ecadaf5</a> for the creation from the <code>GRTSmh_brick</code> data source. A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>. Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).

opencc-zeroJul 2019View details →

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A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.

Contents of the dataset The dataset includes the set of urine samples in .mea format, which can be read using the GCIMS R package. It also contains analytical standards in the same format, used for quality control of the equipment and as a retention time alignment reference. If you want to preview the data, you do not need to download the full Urines.zip and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole dataset. Besides the actual measurements, you will find the annotations.csv and reference_peaks.csv files, with sample annotations and some reference peaks identified in the samples. See further details below. Sample collection Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects were diagnosed with colorectal cancer, 14 subjects were controls. The study protocol was approved by the Ethics Committee of Hospital de Reus (study approval no. 074/2018). Samples were aliquoted and frozen at -80ºC for storage. Sample preparation   Sample preparation improves urine preservation by blocking bacterial growth in the urine, and favours volatile extraction. It also adds an internal standard for verification of instrument variability. Stock solution preparation Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with deionized water until the final volume is 50mL. The HCl 5M is used to obtain an acid pH. The pH is controlled with a pH test paper. The final pH level must be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits the bacterial growth in the urine. Internal standard solution preparation   The 4-flurobenzaldehyde is located in retention time around 200 seconds and can be used as an internal standard. Prepare a methanol stock solution using 100 ml of methanol grade for preparative chromatography and 200 ml of distilled water. Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution. Dilute the previous mixture in 400 mL of mili-Q water. Sample preparation   Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution were added to the urine sample, and 1.5 ml of the acidified urine sample were transferred into a 20ml vial, ensuring only the supernatant of the sample is transferred. Finally, 20 mL of the internal standard solution is added to the sample. GC-IMS Analysis Samples were analyzed with a GC-IMS FlavourSpec® instrument from G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes. Besides the urines, a set of measurements of a ketone mixture was also analyzed at least once per day as an analytical standard control of the equipment. The mixture included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as the urine samples. Samples are provided in the native instrument format (.mea format), that can be read with the GCIMS R package or with the instrument software. Sample annotations The dataset includes a CSV file with sample annotations. The annotations include the following information: <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> Reference peaks Some peaks were manually annotated to ease the alignment of the samples and explore alignment solutions. While manual peak labelling is not generally required, we attach those reference peaks as well and their locations, in case they are of interest. These reference peaks are found at reference_peaks.csv.

opencc-by-4.0Feb 2023View details →

zenodo44/100

High-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category: Dataset and Code

Dataset of identified high-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category, as well as the associated code to reproduce the figures of accompanying publication. The data have been recorded by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Array. Accompanying publication: Taenzer, L.L., Gawarkiewicz, G., and Plueddemann, A. (2023). Categorization of High-Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf. Journal of Geophysical Research: Oceans, 128, e2022JC019625. https://doi.org/10.1029/2022JC019625Contact: Lukas Taenzer (lukas.taenzer@whoi.edu)Structure of provided code:<ul><li>PART A: Local high-wind ocean impact analysis</li><li>PART B: Analysis of seasonal high-wind impacts on stratification</li><li>PART C: High-wind event categorization and the impact of different categories</li></ul>Code has been written in MATLAB R2023a.Output:<ul><li>Processed data of all locally detected high-wind events incl. scalar forcing and shelf impact estimates as well as their corresponding high-wind event category:<ul><li>'OOIcp_HighWindEvents_ScalarMetrics.nc' (see userflag 'save_peak_ooi')</li><li>See README_HighWindEvents_ScalarMetrics for further details and license.</li></ul></li><li>Figures 2, 3, 4, 5, 6, 7, 8, and 9 of accompanying publication<ul><li>saved as .png file (always)</li><li>saves as .eps file (see userflag 'save_fig_eps')</li></ul></li></ul>Input for Analysis:<ul><li>Gridded Hydrography and Bulk Air-Sea interactions time series observed by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) (Taenzer et al., 2023). The required fields to reproduce the results of the accompanying publication are provided:<ul><li>Input/OOIcp_Met_Combined.nc</li><li>Input/OOIcp_CTD_ISSM_stat.nc</li><li>Input/OOIcp_CTD_PMUI_prof.nc</li></ul></li><li>High-wind event categorization based on their spatio-temporal sea level pressure and temporal surface wind stress signatures around/at the OOI Coastal Pioneer Array location:<ul><li>Input/storm_type_2015-2021_v5.mat</li></ul></li></ul>Additional input for reproducing figures:<ul><li>Manually determined cyclone tracks for cyclones that occur during the fall destratification seasons 2015-2021:<ul><li>Input/stormtracks_cyclones_20152021_save.mat</li></ul></li><li>ERA5 sea level pressure data (Hersbach et al., 2018) on a 6-hour temporal and a 1°x1° spatial resolution for the time period 2015-01-01 to 2022-06-30 and across the Eastern US, Canada, and the Northwest Atlantic with the OOI Coastal Pioneer Array in the center<ul><li>Input/ERA5_6h_2015-2022_region_1x1.mat</li></ul></li></ul>

opencc-by-4.0Jun 2023View details →

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Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"

This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper: "Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."

opencc-by-4.0Jul 2023View details →

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Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.

supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146

opencc-by-4.0Jan 2020View details →

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Automatically computed correspondence patterns among six Burmish languages (based on 500 concepts in Huang 1992)

This document is a printout of automatically computed correspondence patterns amount six Burmish languages (Old Burmese, Longchuan Achang, Xiandao, Atsi, Bola, and Maru). It uses as input 500 concepts taken from Huang 1992.

opencc-by-4.0Feb 2019View details →

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MR Spectra from rat hippocampus with LCModel quantification and the corresponding basis set

This folder contains the LCModel quantifications of spectra acquired in hippocampus from 7 rats. The spectra were quntified using six different DKNTMN (spline stiffness) values (0.1, 0.25, 0.4, 0.5, 1, 5). In the folder Control_files_Basis_set you can find all the control files used in this quantification along with the corresponding basis set (metabolites/simulated using NMRScopeB from jMRUI and in vivo parameters + full MM spectrum). Please cite the following manuscript if you are using the data <a href="https://pubmed.ncbi.nlm.nih.gov/34268821/">In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times - PubMed (nih.gov)</a>

opencc-by-4.0Jun 2020View details →

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Archäologische Chronologie und historische Interpretation: Die Merowingerzeit in Süddeutschland (Correspondence Analysis Data Set)

This data set is a supplement to the book "Archäologische Chronologie und historische Interpretation: Die Merowingerzeit in Süddeutschland" (De Gruyter, 2016) and comprises the archaeological data and the results of the correspondence analysis of Merovingian-period graves from southern Germany and their chronological classification. The data sets for female and male burials can be downloaded as PDF, EXCEL and CSV files.

opencc-by-nc-4.0Aug 2016View details →

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Corresponding spreadsheet to the Paper 'An intersectional approach to analyse gender productivity and open access: a bibliometric analysis of the Italian National Research Council' submitted to the Scientometric journal by Roberta Ruggieri, Fabrizio Pecoraro and Daniela Luzi from National Research Council, Italy.

Gender equality and Open Access (OA) are priorities within the European Research Area (ERA) and cross-cutting issues in European research program H2020. Gender and openness are also key elements of Responsible Research and Innovation (RRI). However, despite the common underlying targets of fostering an inclusive, transparent and sustainable research environment, both issues are analysed as independent, unrelated topics. This paper represents a first exploration of the inter-linkages between gender and OA analysing the scientific production of researchers of the Italian National Research Council under a gender perspective integrated with the different OA publications modes. A bibliometric analysis was carried out for articles published in the period 2016-2018 and retrieved from the Web of Science. Results are presented constantly analysing CNR scientific production in relation to gender, disciplinary fields and OA publication modes. These variables are also used when analysing articles that receive financial support.  Our results indicate that gender disparities in scientific production still persist in particularly in STEM disciplines (Science, Technology, Engineering and Mathematics), while in medical and agricultural sciences the gender gap is the closest to parity. A positive dynamic toward OA publishing and women scientific production is shown when open disciplines with well-established practices are related to articles supported by funds. A slightly higher women propensity toward OA is shown when considering Gold OA,OA or authorships with women in the first and last article by-line position. Moreover, the prevalence of Italian funded articles with women’s contributions published in Gold OA journals seems to confirm this tendency, especially if considering the week enforcement of the Italian OA policies.

opencc-by-4.0Nov 2020View details →

ScienceDex guides

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

Compare curated 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.

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.

Last verified 2026-04-30Open record

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