Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,343
datasets available to search
ShareScore release 0.9.0
Dataset results
13,343 results for “Primary”
Mapping the intertidal microphytobenthos Gross Primary Production
<p>Dataset used in the papers "Mapping the intertidal microphytobenthos Gross Primary Production. PartI & PartII" published in Frontiers Marine Science Research Topic "Advances and Challenges in Microphytobenthos Research: From Cell Biology to Coastal Ecosystem Function", and maps (geotiff) resulting of the use of the GPP-algorithm with Platt or Eilers and Peeters models.</p>
Data relating to Chiedozie et al. How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England.
<p>Data in .csv format relating to the paper Chiedozie et al. 2020 "How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England." Also contains eTables 5-8 in Excel format, and Stata do file for deriving the DU90% indicator.</p>
Supplementary material 3: World Spider Catalog Bibliographic Data: Treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
List of journal/publisher by ranked by treatment count exported from the World Spider Catalog 14 October 2014 with total treatments by source, cumulative treatments, and cumulative proportion of treatments.
Supplementary material 2: World Spider Catalog Bibliographic Data: Publications from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Ranked list of journal/publisher exported from the World Spider Catalog 14 October 2014 with total articles by source, cumulative articles, and cucmulative proportion of articles.
Data set for the physical, chemical and biochemical modelling of the primary sedimentation tanks at the WWTP of Eindhoven
<p>These files contain data about measurement campaigns on the primary sedimentation tanks of the WWTP of Eindhoven (The Netherlands) in 2013 and 2014 and the routinely collected data for 2011 till 2013.</p> <p>The data was processed in the PhD of Youri Amerlinck, entitled "Model refinements in view of wastewater treatment plant optimization: improving the balance in sub-model detail."</p> <p>http://www.biomath.ugent.be/biomath/publications/download/amerlinckyouri_phd.pdf</p> <p><br> WWTP of Eindhoven PST Routine Measurements 2011_2013.csv<br> January 5, 2011 - June 14, 2013: <br> Routine analysis for BOD5, COD, TKN, TP, PO4, TSS</p> <p>WWTP of Eindhoven PST Reduced Capacity 2013.csv<br> June 24, 2013 - July 23, 2013 - September 9, 2013<br> Evaluation of reducing the capacity of the PST (including dosing of chemicals) for CODT, CODS, TP, PO4 ,TSS </p> <p>WWTP of Eindhoven PST measurement campaign full ASM 20140506.csv<br> May 6, 2014: <br> Full ASM fractionation BOD5, CODT, CODS, TSS, VSS TP, PO4 ,TN, NH4, NO3, pH</p> <p>WWTP of Eindhoven PST measurement campaign full ASM and Cations 20140902.csv<br> September 2, 2014:<br> Full ASM fractionation (repetition) and cation analysis (BOD10, CODT, CODS, TSS, VSS, TP, PO4 ,TN, NH4, NO3, pH - Ca, Mg, Na, K, Fe)</p>
Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013
<ul><li>This dataset is generated by the advanced CASA model, encompassing vegetation net primary productivity (NPP) raster data for the Tibetan Plateau from 1982 to 2013. The model's input parameters comprise NDVI time series, monthly average temperature, monthly total precipitation, monthly total solar radiation, and vegetation type. The data is provided at an 8 km × 8 km spatial resolution and is formatted in ENVI format (.dat).</li><li>Remarkably, during cross-validation with the MODIS 500m resolution product (MOD17A3HGF.061), it exhibited significantly strong correlations, with the correlation coefficients (R) ranging from 0.74 to 0.82.</li><li>Please cite this dataset as<br>Tan, Q., Sun, G., & Pang, Y. (2023). Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013 (V1.0) [Data set]. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.10040818">10.5281/zenodo.10040818</a></li></ul>
Great Britain's primary substation service areas and annual domestic energy statistics
<p>This geospatial data is a combination of Great Britain's 4436 primary substation service areas which have been parsed into a single shapefile for energy systems analysis. The original component datasets were provided by the six distribution network operator (DNO) companies in Great Britain (National Grid Electricity Distribution, Electricity North West Ltd, Scottish and Southern Electricity Networks, UK Power Networks, Scottish Power Energy Networks and Northern Power Grid). Attribution is given to the original data owners at each of these six DNOs and the resulting dataset from this work has been created and published under an open licence with each DNO's permission. </p><p>The data is available to download as two geojson files in the WGS84 coordinate system. One is a streamlined version which just contains the polygons along with a unique primary identifier (UPID), primary substation name, DNO licence area and local authority. The other contains the polygons along with richer energy data which was aggregated to the primary substation level from publicly available Department for Energy Security and Net Zero, Office for National Statistics and National Grid ESO datasets. This data is also available to download in tabular form as a csv file. The meter numbers and consumption values are the means of those reported from 2015-2020. The substation polygons were those as received or publicly available as of the time period of this study (2021-22).</p><p>The pre-print manuscript of the methodology used to create this dataset can be found on arXiv at:</p><p>https://doi.org/10.48550/arXiv.2311.03324</p><p>Funding to support this work was received from the Engineering and Physical Sciences Research Council (EP/W008726/1) under the Gas Net New project and the Alan Turing Institute's Science of Cities and Regions Programme. Thanks are also given to the contributors of QGIS and the Geopandas Python library, both of which were used in this analysis. </p>
Party primaries in Spain
<p><i>Original dataset comprising over 300 selection processes of candidates and leaders in Spain from 1991 to 2021 at both the national and regional level.</i></p>
Net primary production from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms - HYCOM MLD 0.125 Criterion
<p>Net primary production (mg C m<sup>-2</sup> d<sup>-1</sup>) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HYCOM using the density criterion of 0.125 kg m<sup>-3</sup>. </p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <p>Fixed minor issues with:</p> <ol> <li>Conversion of bbp(443) to phytoplankton carbon.</li> <li>Missing values at ~180W.</li> </ol> <p> </p> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2023.</li> <li>Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.</li> <li>Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.</li> <li>File structure compressed using Zlib.</li> </ol> <p>Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.</p> <p> </p> <p>No further updates are planned for this data product - please email tryankeogh@csir.co.za if you have interest in further updates.</p>
Net primary production from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms - HYCOM MLD 0.030 Criterion
<p>Net primary production (mg C m-2 d-1) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HYCOM using the density criterion of 0.030 kg m-3. </p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <p>Fixed minor issues with:</p> <ol> <li>Conversion of bbp(443) to phytoplankton carbon.</li> <li>Missing values at ~180W.</li> </ol> <p> </p> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2023.</li> <li>Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.</li> <li>Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.</li> <li>File structure compressed using Zlib.</li> </ol> <p>Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.</p> <p> </p> <p>No further updates are planned for this data product - please email tryankeogh@csir.co.za if you have interest in further updates.</p>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
Net primary production from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms - HADLEY MLD 0.125 Criterion
<p>Net primary production (mg C m-2 d-1) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HADLEY EN4.2.2 using the density criterion of 0.125 kg m-3. </p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <p>Fixed minor issues with:</p> <ol> <li>Conversion of bbp(443) to phytoplankton carbon.</li> <li>Missing values at ~180W.</li> </ol> <p> </p> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2023.</li> <li>Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.</li> <li>Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.</li> <li>File structure compressed using Zlib.</li> </ol> <p>Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.</p> <p> </p> <p><strong>Version 1.3</strong></p> <ol> <li>Updated to include 2024.</li> </ol>
Net primary production from the Lee-AbPM algorithm
<p>Net primary production (mg C m-2 d-1) calculated from the Lee-AbPM algorithm.</p> <p>Data on a regular 25km grid at a 8 day resolution.</p> <p><strong>Version 1.1</strong></p> <ol> <li>Updated to include 2023.</li> </ol> <p><strong>Version 1.2</strong></p> <ol> <li>Updated to include 2024.</li> </ol>
Dataset from Holding et al. (2019)––Seasonal and spatial patterns of primary production in a high latitude fjord
<p>Unprecedented melting of the Greenland Ice Sheet (GrIS) is impacting the coastal ocean, and its effects on fjord ecology remain understudied. It has been suggested that as glaciers retreat, primary production regimes may be altered, rendering fjords less productive. Here we present data from the paper Holding et al. (2019). Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. <em>Biogeosciences</em>, <em>16</em>(19), 3777-3792, /doi.org/10.5194/bg-16-3777-2019. This paper investigates patterns of primary productivity in a northeast Greenland fjord (Young Sound, 74°N), which receives run-off from the GrIS via land-terminating glaciers. This dataset includes measures of size fractioned primary production and chlorophyll <em>a </em>biomass, as well as CTD data and biochemical parameters. Furthermore, primary production was measured using photosynthesis v. irradiance (PI) curves, thus PI curve parameters are also available. The data were taken during the ice-free season along a spatial gradient of meltwater influence. </p> <p>We thank Egon Frandsen, Kunuk Lennert, and Ivali Lennert for excellent assistance during fieldwork. This research has beensupported by the Danish Environmental Protection Agency’s programme for Arctic research (DANCEA) (grant no. MST-112-0023), The Carlsberg Foundation (grant no. 2013_01_0532), the Norwegian Research Council (Mi- croPolar) (grant no. RCN 225956), and the European Commission, H2020 Research Infrastructures (GrIS-Melt (grant no. 752325) and INTAROS (grant no. 727890)). </p>
The genomic and transcriptional landscape of primary central nervous system lymphoma
<p>Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Despite extensive research, the molecular alterations leading to PCNSL have not been fully elucidated. In order to provide a comprehensive description of the genomic and transcriptional landscape of PCNSL, we here performed whole-genome and transcriptome sequencing and integrative analysis of 51 lymphomas presenting in the CNS, including 42 EBV-negative PCNSL, 6 secondary CNS lymphomas (SCNSL) and 3 EBV+ CNSL and matched controls. The results were compared to an independent validation cohort of 31 FFPE CNSL specimens (PCNSL, n = 19; SCNSL, n = 9; EBV+ CNSL, n = 3) and 36 systemic DLBCL cases outside the CNS.</p> <p>This repository contains tab separated value text files:</p> <p>- Radke_et_al_supplementary_somatic_CNVs.tsv (somatic copy number variations predicted by ACEseq)<br> - Radke_et_al_supplementary_somatic_indels.tsv (somatic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_indels_exonic.tsv (somatic exonic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_mutations_integrated.tsv (table of gene by patients, stating which mutations were observed) <br> - Radke_et_al_supplementary_somatic_mutations_integr_integrated_including_kataegis_counts.tsv (table of gene by patients, stating which mutations were observed, including the count of mutations falling into kataegis hotspots) <br> - Radke_et_al_supplementary_somatic_SNVs.tsv (somatic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_exonic_functional.tsv (somatic exonic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_rescued_by_TiNDA (mutations initially classified as germline, but likely tumor mutations based on VAF modelling by TiNDA)<br> - Radke_et_al_supplementary_somatic_SVs.tsv (somatic structural variations predicted by the DKFZ Sophia workflow)<br> - Radke_et_al_supplementary_RNAseq_numReads_CNSLs.tsv (RNAseq read counts calculated by the DKFZ RNAseq workflow)</p> <p>This repository contains the raw unedited images from the manuscript:</p> <p>- Radke_et_al_Main_Figure_1c_BCL6.tif (raw unedited image for Main Figure 1c - BCL6)<br> - Radke_et_al_Main_Figure_1c_CD10.tif (raw unedited image for Main Figure 1c - CD10)<br> - Radke_et_al_Main_Figure_1c_MUM1.tif (raw unedited image for Main Figure 1c - MUM1)<br> - Radke_et_al_Supplementary_Figure_1a_CD20.tif (raw unedited image for Supplementary Figure 1a - CD20)<br> - Radke_et_al_Supplementary_Figure_1a_EBV.tif (raw unedited image for Supplementary Figure 1a - EBV)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(FFPE).tif (raw unedited image for Supplementary Figure 1a - HE (FFPE))<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_1.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 1)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_2.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 2)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_3.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 3)<br> - Radke_et_al_Supplementary_Figure_1a_Ki67.tif (raw unedited image for Supplementary Figure 1a - Ki67)<br> - Radke_et_al_Supplementary_Figure_1b_EBV_PCR.pptx (raw unedited image for Supplementary Figure 1b - EBV PCR )<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_1.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 1)<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_2.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 2)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-033.tif (raw unedited image for Supplementary Figure 7h - PD-L1 LS-033)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-031.tif (raw unedited image for Supplemnetary Figure 7h - PD-L1 LS-031)</p>
Zellige example dataset: primary culture of human bronchial cells infected by SARS-CoV-2
<p>A human bronchial epithelium was infected by SARS-CoV-2. The specimen was imaged four days post-infection. The z-stack image encompasses the very irregular epithelium surface. It was acquired with a point scanning microscope (Zeiss LSM700) equipped with Zeiss Plan-Apochromat 63x lens (NA=1.4). Pixel size 0.110µm, z step 0.4µm. This dataset contains both the ground-truth height map and the height map generated with Zellige. The Zellige parameters used are: <span class="math-tex">\(T_{A}=23, T_{otsu}=16, S_{min}=5, \sigma_{xy}=4, \sigma_{z}=1, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.9, T_{OSE2}=0.1, R_{2}=5, C_{2}=0.8.\)</span>.</p> <p>Nota: to compare the ground truth height map with the Zellige height map, one first needs to substrat 1 to all values of the Zellige height map.</p> <p>See the related paper:<br> <a href="https://hal-pasteur.archives-ouvertes.fr/pasteur-03319522">https://hal-pasteur.archives-ouvertes.fr/pasteur-03319522</a></p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p> <p> </p>
OmniFold Weights | CMS 2011A Open Data | Jet Primary Dataset | pT 375-700 GeV
<p>Unfolding weights corresponding to a selection of jets from the <a href="https://doi.org/10.5281/zenodo.3340205">Jet Primary Dataset of the CMS 2011A Open Data in MOD HDF5 format</a> and associated simulated datasets. The unfolding is performed in a high-dimensional manner using the <a href="https://arxiv.org/abs/1911.09107">OmniFold</a> method, which can unfold all observables simultaneously. <a href="https://arxiv.org/abs/1810.05165">Particle Flow Networks</a> are used in Step 1 and Step 2 of the OmniFold method to process the full phase space information. The datasets and neural networks were accessed/built via the <a href="https://energyflow.network/">EnergyFlow Python package</a>. An upcoming version of the package will contain an example/demo demonstrating how to use these weights.</p> <p>The phase space selections for the data, sim, and gen datasets (using the terminology of the OmniFold paper) are:</p> <ul> <li>data: <span class="math-tex">\(p_T^{\rm jet}\in [375, 700]\)</span> GeV, <span class="math-tex">\(|\eta^{\rm jet}|<2.4\)</span>, jet quality <span class="math-tex">\(\ge\)</span> 2</li> <li>sim: <span class="math-tex">\(p_{T,\text{corr}}^{\rm jet} \in [375, 700]\)</span> GeV, <span class="math-tex">\(|\eta^{\rm jet}| < 2.4\)</span>, gen jet matched ('gen_jet_pts != -1' in EnergyFlow), jets from the <a href="https://doi.org/10.5281/zenodo.3341500">170</a> and <a href="https://doi.org/10.5281/zenodo.3341772">1800</a> MC datasets are excluded</li> <li>gen: Matched to sim jet</li> </ul> <p>The omnifold_weights.npz file contains two arrays, 'wssim' corresopnding to the Step 1 weights <span class="math-tex">\(\omega_n\)</span>, and 'wsgen' corresponding to the Step 2 weights <span class="math-tex">\(\nu_n\)</span>, for iteration <span class="math-tex">\(n\)</span>. The shape of each of these arrays is (6, 16489054), with the first axis being the iteration axis and the second axis being the event axis. There are 5 iterations, but 6 sets of weights in each array, with the 0th entry being the starting weights.</p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Hourly LC impacts - Primary Non-renewable energy - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Primary Non-renewable Energy, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
CMIP6 model vertically-integrated net primary production data
<p>Vertically-integrated net primary production (NPP) data from 12 models that participated in phase six of the Coupled Model Intercomparison Project (CMIP6). All data pulled from the Earth System Grid Federation.</p> <p>All model output was regridded onto a common, regular horizontal grid of 1x1 degrees (360 x 180) in longitude by latitude.</p> <p>Units are mol C per metre squared per second.</p> <p>Models are:</p> <ol> <li>ACCESS-ESM1-5</li> <li>CanESM5</li> <li>CESM2</li> <li>CNRM-ESM2-1</li> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> <li>IPSL-CM6A-LR</li> <li>MIROC-ES2L</li> <li>MPI-ESM1-2-HR</li> <li>MRI-ESM2-0</li> <li>NorESM2</li> <li>UKESM1-0-LL</li> </ol>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.