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zenodo44/100

Magneto-telluric data from the Los Humeros geothermal field in Mexico: time series and edi-files

The data are from the Los Humeros geothermal field in Mexico. The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series. A file containing the location of the soundings and calibration files are also in the dataset. The Metronix equipment was used to acquire the data. The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.

opencc-by-4.0Dec 2019View details →

zenodo44/100

Magneto-telluric data from the Acoculco area in Mexico: time series and edi-files

The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series. The Metronix equipment was used to acquire the data. The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.

opencc-by-4.0Dec 2019View details →

zenodo44/100

Supplementary files for "Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes"

Databases containing DFT optimized structures used for the paper: ''Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes". For further details of the computational setup we refer to this paper. Each database contains structures for one carbon substrate. The structures can be retrieved using the Atomic Simulation Environment (ASE).

opencc-by-4.0Oct 2020View details →

zenodo44/100

Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

opencc-by-4.0Jan 2021View details →

zenodo44/100

1QIsaa data collection (binarized images, feature files, and plotting scripts) for writer identification test using artificial intelligence and image-based pattern recognition techniques

The Great Isaiah Scroll (1QIsaa) data set for writer identification This data set is collected for the ERC project: The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls PI: Mladen Popović Grant agreement ID: 640497 Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a> Copyright (c)     University of Groningen, 2021. All rights reserved. Disclaimer and copyright notice for all data contained on this .tar.gz file: 1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes. 2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed. 3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data. 4) the user should refer to the first public article on this data set: Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476. BibTeX: <pre>@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }</pre> 5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy. Organisation of the data: The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions. The 'images' directory contains NetPBM images of the columns of 1QIsaa. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (arxiv.org/abs/1911.07930) system, and the other one is the manually cleaned version of the binarized output.   The file names for the direct binarized output are of the format '1QIsaa_col<columnnr>.pbm', for example, '1QIsaa_col15.pbm'. And, for the cleaned version, the format is '1QIsaa_col<columnnr>_cleaned.pbm', for example, '1QIsaa_col15_cleaned.pbm'. Note: the image files are not in a separate directory; they will be extracted in the same place. However, due to the unique naming, there is no problem extracting them in one single directory. The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use. The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal. Brief description: According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'.  These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way. Tools: Binarization: The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl) Image Morphing: In the original article, data augmentation was performed using image morphing. The tool is available on GitHub: https://github.com/GrHound/imagemorph.c Features for writer identification: Lambert Schomaker http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html 1. L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798. 2. Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features,  IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.   The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl) If you have any question, please contact us: Maruf A. Dhali <m.a.dhali(at)rug.nl> Lambert Schomaker <l.r.b.schomaker(at)rug.nl> Mladen Popović <m.popovic(at)rug.nl> Please cite our papers if you use this data set: 1. Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476. 2. Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Supplement 1: Full list of ICD10 codes and number of gene-disease links (tab-separated-value file); Supplement 2: Mapping (tab-separated-value file)

Supplements to BioMedBridges deliverable 10.2 A prototype linking ICD10/SNOMED CT concepts to Ensembl gene identifiers: Supplement 1: Full list of ICD10 codes and number of gene-disease links: table_icd10_gene_count_descr.tsv Supplement 2: Mapping of disease terms: ICD10_to_doid.tsv

opencc-zeroJan 2015View details →

zenodo44/100

RAPID input and output files corresponding to "RAPID Applied to the SIM-France Model"

Corresponding peer-reviewed publication This dataset corresponds to all the RAPID input and output files that were used in the study reported in: <ul> <li>David, Cédric H., Florence Habets, David R. Maidment and Zong-Liang Yang (2011), RAPID applied to the SIM-France model, Hydrological Processes, 25(22), 3412-3425. DOI: 10.1002/hyp.8070. </li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Time format The times reported in this description all follow the ISO 8601 format. For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1st 2000 (2000-01-01), Central Standard Time (-06:00). Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step. For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps. The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1st 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1st 2000, Universal Time. Data sources The following sources were used to produce files in this dataset: <ul> <li>The hydrographic network of SIM-France, as published in Habets, F., A. Boone, J. L. Champeaux, P. Etchevers, L. Franchistéguy, E. Leblois, E. Ledoux, P. Le Moigne, E. Martin, S. Morel, J. Noilhan, P. Quintana Seguí, F. Rousset-Regimbeau, and P. Viennot (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research: Atmospheres, 113(D6), DOI: 10.1029/2007JD008548.</li> <li>The observed flows are from Banque HYDRO, Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations. Available at http://www.hydro.eaufrance.fr/index.php.</li> <li>Outputs from a simulation using SIM-France (Habets et al. 2008). The simulation was run by Florence Habets, and produced 3-hourly time steps from 1995-08-01T00:00+02:00 to 2005-07-31T21:02+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> Software The following software were used to produce files in this dataset: <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.1.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> Study domain The files in this dataset correspond to one study domain: <ul> <li>The river network of SIM-France is made of 24264 river reaches. The temporal range corresponding to this domain is from 1995-08-01T00:00+02:00 to 2005-07-31 T21:00+02:00.</li> </ul> Description of files All files below were prepared by Cédric H. David, using the data sources and software mentioned above. <ul> <li>rapid_connect_France.csv. This CSV file contains the river network connectivity information and is based on the unique IDs of the SIM-France river reaches (the IDs). For each river reach, this file specifies: the ID of the reach, the ID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the IDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of ID. The values were computed based on the SIM-France FICVID file. This file was prepared using a Fortran program.</li> <li>m3_riv_France_1995_2005_ksat_201101_c_zvol_ext.nc. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same IDs and are sorted similarly to rapid_connect_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005/07/31T21:00+02:00. The values were computed using the outputs of SIM-France. This file was prepared using a Fortran program.</li> <li>kfac_modcou_1km_hour.csv. This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same IDs and are sorted similarly to rapid_connect_France.csv. The values were computed based on the following information: ID, size of the side of the grid cell, Equation (5) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.</li> <li>kfac_modcou_ttra_length.csv. This CSV file contains a second guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same IDs and are sorted similarly to rapid_connect_France.csv. The values were computed based on the following information: ID, size of the side of the grid cell, travel time, and Equation (9) in David et al. (2011).</li> </ul> <ul> <li>k_modcou_0.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> </ul> <ul> <li>k_modcou_1.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_2.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_3.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_4.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_a.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_b.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_c.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_0.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_1.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_2.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_3.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_4.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_a.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_b.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_c.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>rivsurf_France.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the SIM-France domain. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_adour.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Adour River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_allier.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Allier River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_ardeche.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Ardeche River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_dordogne.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Dordogne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_garonne.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_garonne_reste.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_garonneariege.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne and Ariege River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_herault.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Herault River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_loir.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Loir River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_loire.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_loire_amont_nevers.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_loire_reste.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_lot.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Lot River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_meuse.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Meuse River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_oise.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Oise River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_rhone.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_rhone_reste.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_rhone_suisse.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_saone.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Saone River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_seine.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_seine_amont.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_seine_reste.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_tarn.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Tarn River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>rivsurf_vienne.csv. This CSV file contains the list of unique IDs of SIM-France river reaches in the Vienne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li>Qout_France_201101_c_zvol_ext_3653days_p1_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_3653days_p2_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_3653days_p3_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_3653days_p4_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_3653days_pa_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_3653days_pb_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_3653days_pc_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_366days_p0_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s_pougny.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li>gage_id_1995_1996_full.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>gage_id_1995_1996_full_nash.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record and for which RAPID simulations led to a positive efficiency value. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>gage_id_1995_2005_70.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with 70% daily data record. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>Qobs_1995_1996_full.csv. This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to gage_id_1995_1996_full.csv. The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li>Qobs_1995_1996_full_nash.csv. This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to gage_id_1995_1996_full_nash.csv. The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li>Qobs_1995_1996_full_nash_93.csv. This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to gage_id_1995_1996_full_nash.csv. The time range for the daily values is from 1995-11-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li>Qobs_1995_2005_70.csv. This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to gage_id_1995_2005_70.csv. The time range for the daily values is from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li>Qobsbarrec_1995_1996_full_nash.csv. This CSV file contains the reciprocal of the averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to gage_id_1995_1996_full_nash.csv. The time range for the computation of the average is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li>forcingtot_id_1995_1996_full.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the SIM-France domain. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>forcinguse_id_garonne_reste.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Garonne River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>forcinguse_id_loire_reste.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Loire River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>forcinguse_id_rhone_pougny.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream of Lake Geneva. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>forcinguse_id_rhone_reste.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>forcinguse_id_seine_reste.csv. This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Seine River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li>Qfor_1995_1996_full.csv. This CSV file is identical to Qobs_1995_1996_full.csv.</li> <li>Qfor_1995_1996_full_93.csv. This CSV file is identical to Qobs_1995_1996_full_nash_93.csv.</li> <li>Qinit_93.csv. This CSV file contains the final state of RAPID after a simulation ending on 1995-11-31T00:00+02:00</li> </ul> Known bugs and limitations in this dataset or the associated manuscript. A small bug in RAPID v1.1.0 was discovered and fixed on 2011-07-16 that had an impact on the optimization of parameters when using forcing data to replace upstream simulations. This bug led to erroneous results for only two of the basins where upstream forcing was used: Garonne River Basin, downstream; and Rhone River Basin, downstream. The bug had no influence on: Loire River Basin, downstream, and Seine River Basin, downstream; or on any of the other simulations. This should not affect the conclusions of David et al. (2011) since only a few locations were impacted. Funding This work was partially supported by the French Mines Paristech, by the French Agence Nationale de la Recherche under the Vulnérabilité de la nappe du Rhin (VulNaR) project, by the French Programme Interdisciplinaire de Recherche sur l’Environnement de la Seine (PIREN-Seine) project, by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G, by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems, and by the American Geophysical Union under a Horton (Hydrology) Research Grant.

opencc-by-4.0Oct 2011View details →

zenodo44/100

ALLENMINER v2.1 data files

This dataset accompanies the ALLENMINER software package ( http://fredpdavis.com/allenminer ) that searches gene expression in the adult and developing mouse brain and spinal cord atlases published by the Allen Institute for Brain Science.   ALLENIMNER v2.1 downloads these fastsearch files to accelerate searches within named brain regions.

opencc-zeroDec 2015View details →

zenodo44/100

Aldeghi et al. Files for absolute free energy calculations in gromacs.

These are the files for performing absolute free energy calculations using gromacs as reported in "Accurate calculation of the absolute free energy of binding for drug molecules. Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC. Chem Sci. 2016 Jan 14;7(1):207-218. DOI: 10.1039/C5SC02678D"   The files should prove useful for anyone wishing to try out their own methodology for comparison purposes or even just to repeat the work on a known dataset.  The data is presented as a zip archive that should unpack into a directory called "Aldeghi-et-al-chemical-science-2016".    There are four sub-directories in there and a README.txt file which should explain the details of the data.

opencc-zeroJul 2016View details →

zenodo44/100

Output files corresponding to "Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities"

Corresponding peer-reviewed publication This dataset corresponds to the output files that were produced for the study reported in: <ul> <li>Sawyer, Audrey H., Cédric H. David, and James S. Famiglietti, (2016), Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities, Science, 353(6300), 705-707. DOI:10.1126/science.aag1058. </li> </ul>   When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.    Data sources The following sources were used to produce files in this dataset: <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php.  Regions used are: Northeast (NE: 01), Mid-Atlantic (MA: 02), South-Atlantic North (SAN: 03N), South-Atlantic South (SAS: 03S), South-Atlantic West (SAW: 03W), Lower Mississippi (MS: 08), Texas (TX: 12), California (CA: 18), and Pacific Northwest (NW: 17).</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS.  Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2010 Census dataset (CENSUS 2010), obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul>   Description of files The files in this dataset contain are described below: <ul> <li>NHDFlowline_CONUS_coastline.zip.  This zip file contains a shapefile with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in regions used. </li> <li>Catchment_CONUS_coastline.zip.  This zip file contains a shapefile with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in regions used. </li> <li>Catchment_CONUS_coastline_centroid.zip.  This zip file contains a shapefile with the centroids of the above catchments. </li> <li>SGD_Coastal_Vulnerabilities.csv.  This .csv file contains the following data (units are in parentheses): <ul> <li>COMID. Unique feature identifier in NHDPlusV2 (-).</li> <li>LENGTHkm. Length of coastline feature (km).</li> <li>REACHCODE. Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature (-).</li> <li>AREAsqkm. Area of coastal catchment feature (km2).</li> <li>REGION. NHDPlusV2 region: NE = Northeast, MA = Mid-Atlantic, SAN = South Atlantic North, SAS = South Atlantic South, SAW = South Atlantic West, TX = Texas, MS = Lower Mississippi, CA = California, PN = Pacific Northwest (-).</li> <li>RLENGTHkm. Total length of coastline accumulated by REACHCODE (km).</li> <li>RAREAsqkm. Total area of coastal catchment accumulated by REACHCODE (km2).</li> <li>BGRUNkgpsqm. Average annual infiltrating runoff for REACHCODE (kg/m2)</li> <li>SGDsqmpy. Average annual fresh SGD rate for REACHCODE (m2/y).</li> <li>RCOUNT. Number of features by REACHCODE (-).</li> <li>PDENpsqkm. Population density for coastal catchment feature (km-2).</li> <li>SWIVULN.  Vulnerability to saltwater intrusion: - 1 = vulnerable, 0 = not vulnerable (-).</li> <li>PCTDEV11.  Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>CONTVULN. Vulnerability to offshore contamination associated with direct groundwater discharge: - 1 = vulnerable, 0 = not vulnerable (-).</li> </ul> </li> </ul>   Known bugs and limitations in this dataset or the associated manuscript. No bugs have been unveiled since publication of this dataset or the associated manuscript.  Vulnerability thresholds are subjective and could be adjusted for different applications, refer to published manuscript for approaches used here.   Funding This work was supported by the Ohio State University School of Earth Sciences, and NSF grant EAR-1446724 (A.H.S); the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and grants from the NASA SWOT and Sea Level Science Teams (C.H.D. and J.S.F.).

opencc-by-4.0Aug 2016View details →

zenodo44/100

Object-based audio scene files for variations of the spatial arrangement in pop mixes for Wave Field Synthesis

This entry contains object-based audio meta-data to generate the mixes published at http://dx.doi.org/10.5281/zenodo.61000. Have a look at README.md for further details.

opencc-by-4.0Aug 2016View details →

zenodo44/100

Audio files referred to in Roettger, Timo B. (2017). Tonal Placement in Tashlhiyt - How an intonation system accommodates to adverse phonological environments.

Audio files referred to in Roettger, Timo B. (2017). Tonal Placement in Tashlhiyt - How an intonation system accommodates to adverse phonological environments. Berlin: Language Science Press (DOI 10.5281/zenodo.814472)

opencc-by-4.0Jun 2017View details →

zenodo44/100

Supplementary data files for manuscript titled "From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research"

<div>This data repository contains all the necessary supplementary files for the manuscript titled "From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research."</div> <div> </div> <div>The repository is a copy of the <a href="https://github.com/IMI-COMBINE/template2graphs">GitHub page</a> with the source code used to generate the graph and additional files required for the Lab Data Template.</div> <div> </div> <div>Below we provide a brief overview of the data files in the `additional folder` and their underlying purpose:</div> <div> <ul> <li>The Data Survey collects relevant project and data set information to set up a Data Management Plan. It can serve as an input for Lab Data Template development.</li> <li>The Lab Data Templates facilitate the collection of AMR research data (in vivo and in vitro) in several sub-tables. The Excel format is compatible with upload procedures into the data repository 'grit' and serves as input for a knowledge graph workflow.</li> <li>The Data dictionary is connected to the Lab Data Templates and ensures harmonized data entries. In addition, the dictionaries collect metadata beyond the content of the Lab Data Template (e.g. bacterial strain information or compound information) and link to ontologies where possible.</li> <li>The FAIR assessments have been used as a primer for improving the template. This report is generated using the FAIR-DSM model.</li> </ul> </div> <div>The templates have been used during the IMI2 GNA NOW project to collect information and have been improved according to FAIR standards in collaboration with the IMI FAIRplus project ("post FAIRification").</div>

opencc-by-4.0Jul 2024View details →

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MelanoDB: Dataset files of clinical and molecular features of advanced melanoma patients treated with MAPK inhibitors

MAPK inhibitors have significantly improved overall survival in patients with metastatic melanoma disease but their efficacy is still limited by primary or acquired resistance. Several studies have attempted to predict response to MAPK inhibitor therapy, however the lack of a consistent cohort prevents better definition of associations between treatment efficacy and clinical and/or molecular features. Here, we present MelanoDB, a collection of patients with metastatic melanoma treated with MAPK inhibitors. We formatted data from 8 different studies for a total of 417 cases to gather common clinical and molecular features. Whole or partial exome sequencing is available for 191 cases and gene expression for 132 cases. We provide a web application to explore the integrated data and its distribution among the collected studies, and we share this dataset to the scientific community according to FAIR principles These data are available under the licence CC-BY-SA. Here we provide a web application viewer of the database content: http://melanodb-ircm.montp.inserm.fr/ You are requested to cite this repository in the case of using these data in a publication.

opencc-by-4.0Jul 2024View details →

zenodo44/100

Updated Files from the revision 1 (986 cMAGs and prokka annotations, all supplemental files)

Included are the following files 1) Assembly files (47) in the form of .fasta for each of the 47 different samples (8 participants x 5 or 6 time points). This is the results of the assembly from hybrid long read data (all Pacbio Revio + ONT Promethion > Q20). Assemblies performed using metaMDBG. This file includes all contigs from the pipeline described in the manuscript and will include both high-quality and complete MAGs.  "hybrid_assemblies.tar.gz" 2) Assembly files (40) in the form of .fasta for the short read metaspades and tell-seq assembly methods "SR_tellseq_assemblies.tar.gz" "SR_metaspades_assemblies.tar.gz" 3) Assembly files from sub-sampling experiment: 3 samples (A6, D5, and H6) were deep sequenced with both PB Revio and ONT Promethion lsk114 R10.4.1 SUP400. Data was randomly subsampled to various depths (1, 5, 10, 20, 30, 40 Gbp, and all) and then assembled with both metaMDBG and metaFlye.  -A6: total of 26 files (PB was less than 40 Gbp thus no 40 Gbp subsampling depth) -D5: total of 28 files -H6: total of 28 files -total: 82 files "LR_ONTPB_sub_assemblies.tar.gz" 4) Tar file of all circular contigs from assemblies. This will include 47 separate fasta files (1 for each participant and time point). This data was used for the viral and plasmid analyses.  "Hsap_circ_contigs_47_assemblies.tar.gz" 5) File containing all of the 985 cMAGs with annotations generated using prokka "985cMAGs_prokkaannotation.tar.gz 6) Deep taxonomic profiling feature table: Pacbio samples (45) with features classified using the GTDB and 985 cMAGs custom database "PB_985cMAG-sourmash_45s_2162f" #feature table "mapping-file_PB45s_2162f_deID.txt" #mapping file, deidentified 7) tar file containing all genomes in the updated quality 'c986 cMAGs' (>90% completeness, <5% contamination, contig=1 8) tar file containing all prokka annotation files associated with the 986 cMAGs 9) All supplemental tables or data files used in the revision

opencc-by-4.0Aug 2024View details →

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IAGOS-CARIBIC MS files collection (v2025.07.11)

<h2>Content</h2> IAGOS-CARIBIC_MS_files_collection_20250711 contains merged IAGOS-CARIBIC data, on a 10s grid (CARIBIC-1 and CARIBIC-2; <https://www.caribic-atmospheric.com/>). There is one netCDF (version 4) file per IAGOS-CARIBIC flight. Files were generated from NASA Ames 1001 source files. For detailed content information, see global and variable attributes. Global attribute `na_file_header` contains the original NASA Ames file header as an array of strings. <h2>Data Coverage</h2> The data set covers 22 years of CARIBIC data from 1997 to 2020, flight numbers 1 to 591. There is no data available after 2020. Also, there is no data available for the following flight numbers within the [1..591] range: <ul> <li>CARIBIC-1, 1997-2002, 1-97: 4, 5, 6, 21, 28, 30, 31, 32, 38, 39, 73, 81, 83, 91, 95</li> <li>98 to 109 do not exist</li> <li>CARIBIC-2, 2005-2020, 110-591: 217, 276, 277, 318, 320, 410, 411, 412, 425, 426, 427, 428, 434, 435 436</li> </ul> <h3>Special note on CARIBIC-1 data</h3> CARIBIC-1 data only contains a subset of the variables found in CARIBIC-2 data files. To distinguish those two campaigns, use the global attribute 'mission'. <h2>File format</h2> netCDF v4, created with xarray, <https://docs.xarray.dev/en/stable/>. Compression: zlib, level 5. Metadata conventions: CF-1.10, ACDD-1.3 (see also 'comment'  global attribute). <h2>Authors and Parameters Info</h2> See `CARIBIC-MS_files_species-and-contributors.csv` in the zip archive. <h3>Primary Contact</h3> <ul> <li>Andreas Zahn, IAGOS-CARIBIC Coordinator , <andreas.zahn@kit.edu></li> <li>Florian Obersteiner, data management, <florian.obersteiner@kit.edu></li> </ul> <h2>Data Availability </h2> This dataset is also available via the KIT-IMKASF THREDDS server, <https://thredds.atmohub.kit.edu/thredds/catalog/iagos-caribic/catalog.html>. <h2>Changelog</h2> <ul> <li>`2025.07.11`: extend netCDF metadata (CF-1.10, ACDD-1.3), revise standard names and units. Data unchanged.</li> <li>`2025.06.02`: introduce netCDF file compression (zlib, level 5). Data unchanged.</li> <li>`2025.03.25`: variable name change (PosLat => lat, PosLong => lon, pstatic => p, Altitude => alt), add acetonitrile and acetone measurement precision columns</li> <li>`2024.10.28`: revise CARIBIC-1 data, all flights (note on lat/lon inaccuracy, range checks for static pressure and temperature). CARIBIC-2 data unchanged.</li> <li>`2024.07.17`: revise ozone data for flights 294 to 591</li> <li>`2024.01.12`: revise naming convention of (nc attributes), add POF IV funding reference (zenodo)</li> <li>`2023.11.15`: add CARIBIC-1 data, revise variable long names</li> <li>`2023.10.17`: remove duplicate flights ("MSA" is "MAA"), add previously missing flights (200, 201)</li> <li>`2023.09.26`: extend data; include soot photometer measurements</li> <li>`2023.07.26`: initial upload</li> </ul>

opencc-by-4.0Oct 2024View details →

zenodo44/100

Data Citation Corpus Data File

Data file for the fourth release of the Data Citation Corpus, produced by DataCite and Make Data Count as part of an ongoing grant project funded by the Wellcome Trust. <a href="https://makedatacount.org/data-citation/">Read more about the project</a>. The data file includes 10,697,745 data citation records (of which 9,682,257 represent unique dataset-publication pairs) in JSON and CSV formats. The JSON file is the version of record. Data is provided in batches of approximately 1 million records each. The publication date and batch number are included in the file name, ex: 2025-08-15-data-citation-corpus-01-v4.1.json. The data citations in the file originate from the following sources: <ul> <li>DataCite Event Data</li> <li>Chan Zuckerberg Initiative (CZI) Science Knowledge Graph</li> <li>Aligning Science Across Parkinson’s (ASAP)</li> <li>Europe PMC</li> </ul> Each data citation record is comprised of: <ul> <li> A pair of identifiers: An identifier for the dataset (a DOI or an accession number) and the DOI of the publication (journal article or preprint) in which the dataset is cited   </li> <li> Metadata for the cited dataset and for the citing publication  </li> </ul> The data file includes the following fields: <div> <table> <tbody> <tr> <td> Field </td> <td> Description </td> <td> Required? </td> </tr> <tr> <td> id </td> <td> Internal identifier for the citation </td> <td> Yes </td> </tr> <tr> <td> created </td> <td> Date of item's incorporation into the corpus </td> <td> Yes </td> </tr> <tr> <td> updated </td> <td> Date of item's most recent update in corpus </td> <td> Yes </td> </tr> <tr> <td> repository </td> <td> Repository where cited data is stored </td> <td> No </td> </tr> <tr> <td> publisher </td> <td> Publisher for the article citing the data </td> <td> No </td> </tr> <tr> <td> journal </td> <td> Journal for the article citing the data </td> <td> No </td> </tr> <tr> <td> title </td> <td> Title of cited data </td> <td> No </td> </tr> <tr> <td> publication </td> <td> DOI of article where data is cited </td> <td> Yes </td> </tr> <tr> <td> dataset </td> <td> DOI or accession number of cited data </td> <td> Yes </td> </tr> <tr> <td> publishedDate </td> <td> Date when citing article was published </td> <td> No </td> </tr> <tr> <td> source </td> <td> Source where citation was harvested </td> <td> Yes </td> </tr> <tr> <td> subjects </td> <td> Subject information for cited data </td> <td> No </td> </tr> <tr> <td> affiliations </td> <td> Affiliation information for creator of cited data </td> <td> No </td> </tr> <tr> <td> funders </td> <td> Funding information for cited data </td> <td> No </td> </tr> </tbody> </table> </div>   Additional documentation about the citations and metadata in the file is available on the <a href="https://makedatacount.org/find-a-tool/data-citation-corpus-documentation/">Make Data Count website</a>.  Notes on v4.1: Version 4.1 of the Data Citation Corpus is a minor update to v4.0 that corrects (1) an error that occurred when a portion of DOI-DOI citations originating from Europe PMC were attributed to the wrong repository, and (2) a small number of DOI formatting errors in the "publication" field. Notes on v4.0: The fourth release of the Data Citation Corpus data file adds new citations from the following sources: <ul> <li> 5.2 million data citations from <a href="https://europepmc.org/">Europe PMC</a> identified as "eupmc" in the source field. Ingest of these citations was performed 9 July 2025. </li> <li> 139,647 data citations from DataCite Event Data for the period 1 January 2025 through 30 June 2025. </li> </ul> This release also includes the following new metadata enhancements: <ul> <li> Affiliation information for cited data from the Gene Expression Omnibus (GEO) repository, reonciled to Research Organization Registry (ROR) IDs where possible. </li> <li> Reconciliation of organization and funders names with the Research Organization Registry (ROR) for new citations from Event Data. </li> <li> Application of Field of Science subject terms to citation records originating from Europe PMC, based on disciplinary area of data repository. </li> </ul> Additional details about the above changes, including scripts used to perform the above tasks, are available in <a href="https://github.com/Make-Data-Count-Community/corpus-data-file" target="_blank" rel="noopener">GitHub</a>.  Additional enhancements to the corpus are ongoing and will be addressed in the course of subsequent releases. Users are invited to submit feedback via <a href="https://github.com/Make-Data-Count-Community/data-citation-corpus-feedback">GitHub</a>. For general questions, email <a href="mailto:info@makedatacount.org">info@makedatacount.org</a>.

opencc-zeroJan 2024View details →

zenodo44/100

Video, image, and supplemental files linked in Burge et al. (2023) "Depredation by Bottlenose Dolphins Tursiops truncatus from Antillean Z-traps at Discovery Bay, Jamaica"

Video, image, and supplementary text files linked in Burge et al. (2023), Caribbean Naturalist, 95: 1–25.Depredation by Bottlenose Dolphins Tursiops truncatus from Antillean Z-traps at Discovery Bay, JamaicaAll video and image files referred to in the main text, figures, and tables are available from this repository. See Table 1 and Table S1 for additional details.

opencc-by-4.0Oct 2023View details →

zenodo44/100

CWS Files

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2023View details →

zenodo44/100

Simulated Ancient Genomic Kinship Dataset: VCF and BAM (1x) Files for Related (including inbred) Pairs

Simulated Ancient Genomic Kinship Dataset: VCF and BAM (1x and 5x) Files for Related (including inbred) PairsDescription:This dataset comprises simulated pedigrees (VCF files containing 8,677,101 autosomal biallelic and 298,625 X chromosomal SNP positions) generated using Ped-sim (v1.3) and comprising pairs of diverse familial relationship types up to third-degree. The first-degree relationships are parent-offspring and siblings; the second-degree relationships are half-siblings, grandparent-grandchild, and avuncular pairs; and third-degree relationships are first cousins, great-grandparent-great-grandchild, and grand avuncular pairs. For each of these 8 relationship types, our dataset includes 48 pairs of individuals. It also contains unrelated pairs. Additionally, the dataset includes first- and second-degree relatives, with inbreeding (parent-offspring pairs where the parents of the offspring are the first cousins and grandparent-grandchild pairs where the grandchild is the offspring of first cousins). Our simulations encompass all combinations of kinship types regarding sex. The dataset was further enriched by simulating ancient DNA-like sequencing data (5x and 1x BAM files) of Ped-sim simulated individuals using the gargammel tool, employing procedures akin to standard paleogenomic sequencing libraries. Note that the BAM files contain only randomly chosen 200K autosomal SNP positions. Positions can be found in the "200K_positions" file. Details can be found in Aktürk, Mapelli and Güler et al. 2023.Data Sources and Generation:Founder genotypes for pedigree simulation were created from the Tuscany (TSI) population SNPs within the 1000 Genomes Dataset v3. Notably, the founder genotypes lack background relatedness or runs of homozygosity (ROH).Description of File Naming Conventions:The naming conventions of the BAM files in this dataset are designed to convey key information regarding the specifics of each file.cov1x or cov5x: This segment denotes the coverage level of the BAM files, indicating whether the sequencing coverage for the individuals in the files is 1x or 5x.run_*: Signifies the particular batch from which the pedigree and individuals are derived. This name segment also applies to VCF files.parent-offspring_* or similar identifiers: Reflects the origin of the individual from the corresponding VCF file. For instance, "parent-offspring_1" corresponds to the individuals present in the "run_*_parent-offspring_1.vcf" file.parent-offspring* or similar identifiers: Indicates the origin of the individual from the sets within the VCF files. For example, "parent-offspring1" signifies the first set of parent-offspring pedigrees within the VCF file. Note that parent-offspring, grandparent-grandchild, and great-grandparent-great-grandchild and the inbreeding VCFs contain only one set, so this identifier is always 1. This convention can be 1 or 2 for the rest of the pedigrees, as the VCF files contain two sets of related pairs._g*-b*-: Provides information about the individual's generational level within the VCF. This follows the Ped-sim syntax. For example, for parent-offspring type, "_g1-b1-" indicates the first parent (generation 1) within a specific pedigree, and "_g1-b2-" indicates the second parent (generation 1) while "_g2-b1-" represents the offspring (generation 2).Example Naming Structure:For instance, the file "cov1x_run1_parent-offspring_1_parent-offspring1_g1-b1-i1.all.hs37d5.cons.90perc.trimBAM.bam" signifies a BAM file with 1x coverage, originating from "run1," containing individuals from the "run_*_parent-offspring_1.vcf" file (first set of parent-offspring pairs) where "_g1-b1-" designates the first parent in the first generation. The latter half of the name "hs37d5.cons.90perc.trimBAM.bam" is the same across all files.  Note1: Segments such as parent-offspring*_g*-b*- can also be tracked in the naming of the genotype columns in the VCF.Note2: Sexual information within the VCF files is discernible from the genetic data present at X chromosome positions. Individuals carrying two genotypes on the X chromosome are female, while those with a single genotype are male.Note3: Some of the individuals from distinct pedigrees may, in fact, be related due to shared ancestry through common founders. To suit specific research objectives, researchers may need to identify and exclude such relatives if the full dataset is used for kinship estimation.For more details about the dataset's generation process, unique characteristics, or any specific inquiries, our team is available for further information. We welcome and encourage inquiries, aiming to provide comprehensive support and additional details that might aid researchers in utilizing this dataset effectively. Please don't hesitate to contact us for any specific information you may need.This repository contains only VCFs and cov1x BAM and 200K_positions files. The rest of the files can be found at 10.5281/zenodo.10079625 and 10.5281/zenodo.10079685.

opencc-by-4.0Nov 2023View details →

ScienceDex guides

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