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5,946 results for “Stocking”
Rooftop photovoltaic (PV) potential data for the Swiss building stock
<p>The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in:</p> <p>Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” <em>Applied Energy</em> 262 (March 15, 2020): 114404.</p> <p>In the process of generating this dataset, the following aspects were included:</p> <ul> <li>Meteorological conditions in Switzerland (solar radiation, temperature, snow cover)</li> <li>Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model)</li> <li>Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva)</li> <li>The panel and inverter efficiencies, as a function of the solar radiation and temperature</li> </ul> <p>Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document <em>rooftop_PV_CH_metadata_V1.pdf.</em></p> <p><strong>Data description:</strong></p> <p>The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided:</p> <ol> <li>Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. </li> <li>Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude</li> </ol> <p>If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly.</p>
The MAT_STOCKS database: economy-wide material flows and material stock dynamics around the world
<p>Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.</p> <p>This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (<em>MISO2) </em>(Wiedenhofer et al. 2024)<em>. </em>MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository. </p> <p><strong>The MAT_STOCKS database version 1.0 </strong>provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:</p> <ul> <li>Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)</li> <li>Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)</li> <li>Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)</li> <li>177 countries</li> <li>1900 to 2016 </li> </ul> <p>All units in kilotons. Paramter names are in accordance with the system definition given in the publication.</p> <p>Additionally, this repository includes all data presented in the figures of the related journal article.</p> <p><strong>Further information</strong></p> <p>This dataset complements the following scientific article:</p> <p>Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, <a href="https://doi.org/10.1111/jiec.13575">https://doi.org/10.1111/jiec.13575</a></p> <p>The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <em>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</em></p> <p><strong>Funding</strong></p> <p>This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.<br><br></p>
Consumer Stocks: Fish, Vegetation, and other Non-physical Data from Everglades National Park (FCE LTER), South Florida, USA from February 2000 to April 2005
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Consumer Stocks: Physical Data from Everglades National Park (FCE), South Florida from February 1996 to April 2008
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the physical data of the sampled plots.
Consumer Stocks: Fish Biomass from Everglades National Park (FCE), South Florida from February 2000 to April 2005
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Consumer Stocks: Fish Biomass from Everglades National Park (FCE), South Florida from February 1996 to March 2000
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Consumer Stocks: Wet weights from Everglades National Park (FCE), South Florida from March 2003 to April 2008
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Long-term biomass estimates for the central stock of northern anchovy
<p>Long-term biomass estimates for the central stock of northern anchovy (CSNA; <em>Engraulis mordax</em>) in the California Current Ecosystem are estimated from geospatial weighting of egg and larval data from winter/spring CalCOFI surveys (1951–2021). Estimates include the entire range of the CSNA, from northwestern Baja California, Mexico, to north of Point Reyes, California, and nearshore waters.</p>
Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
<p>Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution
<p>Soil organic carbon stock in kg/m<sup>2</sup> for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (<a href="https://doi.org/10.5281/zenodo.1475457">https://doi.org/10.5281/zenodo.1475457</a>), bulk density (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>) and coarse fragments (<a href="https://doi.org/10.5281/zenodo.2525681">https://doi.org/10.5281/zenodo.2525681</a>), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from organic carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Building Stock and Building Typology of Kigali, Rwanda
<p> </p> <p>Dynamically changing urban agglomerations in emerging countries in the Global South experience rapid changes in their urban extent and morphology, due to a growth of population and migration, as well as socioeconomic developments. It is important to have access to updated information on the qualitative and quantitative status of settlements, in order to monitor and inform the housing sector, spatial and infrastructure planning, municipal revenue collection and budgeting, and the supply of social services. Very high-resolution (VHR) multispectral satellite images are one important, cost effective, source of regular, updated, data on urban land-use and built-up areas. The authors acquired a Pléiades satellite image from August 2015 for the central part of the capital of Rwanda, Kigali. Object-based image analysis (OBIA) and expert-based post-classification were then applied to derive building footprints and building heights, and to assign all buildings to nine building archetypes. In a second step, building footprint data from aerial images of the same area in 2008-2009 were analysed, to identify the change of the building stock in the respective period. In total, 165,625 built entities have been detected for 2008-2009 and 211,458 for 2015; this entails a 27.7% increase in the number of buildings. The dataset presented is a completely revised version of a dataset that was used for a published report on the housing supply in Kigali in 2018.</p>
Zagreb Building Stock Model
<p>The dataset Zagreb Building Stock Model is a polygon layer containing data of the name, type, address, and age of buildings. The aim of the dataset is to collect, integrate, and organize data on the age of buildings in Zagreb. The dataset is created from existing datasets of state and city institutions such as cadastral data, official registers of public authorities, historical registers, and other relevant data sources.</p>
Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon
<p>This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include:</p> <ul> <li> <p>new updated global mangrove coverage map (contact Thomas Worthington),</p> </li> <li> <p>spatiotemporal predictions to account for differences in spectral reflectance at the time of field work,</p> </li> <li> <p>additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file).</p> </li> </ul> <p>To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points.</p> <p>Production steps (ensemble predictions using SuperLearner) are explained in detail at: </p> <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> <p>Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>.</p> <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>
Online Data for 'The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century'
<p>This data file (.xlsx) contains all data used to create table 1, figures 1a-d, figure 2, figure S1, S2, and S5 of the study "The role of wildfires in the interplay of forest carbon stocks and wood harvest in the contiguous United States during the 20th century". Main article is available under: https://doi.org/10.1029/2023GB007813</p>
Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the English Channel (ICES area 27.7.d) stock
<p><strong>Contents: </strong></p> <p>This dataset can be completed with the : <strong>Macroscopic, histological and stereological image dataset of the Striped red mullet (<em>Mullus surmuletus</em>) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock</strong>, which can also be found on the Zenodo repository.</p> <p>This dataset contains the macroscopic and histological images of the ovaries of 214 Striped red mullet (female, <em>Mullus surmuletus</em>, Linnaeus 1758) collected from the English Channel stock (ICES area 27.7.d) in February 2021 (n=20), March 2021 (n=13), April 2021 (n=12), May 2021 (n=15), August 2021 (n=15), September 2021 (n=15), October 2021 (n=41), November 2021 (n=10), December 2021 (n=14), January 2022 (n=30), February 2022 (n=15) and August 2022 (n=14).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 621 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 211 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 484 histological slides acquired during this study.</li> <li>Data was split for smaller size downloads : <ul> <li><strong>Histology_slides_1of5 :</strong> histological sections for individuals numbered 001 to 045</li> <li><strong>Histology_slides_2of5 :</strong> histological sections for individuals numbered 046 to 138</li> <li><strong>Histology_slides_3of5 :</strong> histological sections for individuals numbered 154 to 180</li> <li><strong>Histology_slides_4of5 :</strong> histological sections for individuals numbered 196 to 270</li> <li><strong>Histology_slides_5of5 :</strong> histological sections for individuals numbered 271 to 334</li> </ul> </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip :</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration</strong> : Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used.</li> <li><strong>Homogeneity</strong> : Reading results for 96 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 96 slides belong to 16 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total </strong>: Reading results for 214 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li><strong>Macro_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_MULL.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_MULL.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 214 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li><strong>Stereo_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_MULL.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_MULL.csv</strong> : a text data file (.csv) of the stereology count results of 294 slides read during this study. Among these slides, 96 were read to test the homogeneity distribution of different cell types found throughout each ovary (16 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 4 agents). Finally, 214 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul>
Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock
<p><strong>Contents: </strong></p> <p>This dataset can be completed with the : <strong>Macroscopic, histological and stereological image dataset of the Striped red mullet (<em>Mullus surmuletus</em>) ovaries from the English Channel (ICES area 27.7.d) stock</strong>, which can also be found on the Zenodo repository.</p> <p>This dataset contains the macroscopic and histological images of the ovaries of 103 Striped red mullet (female, <em>Mullus surmuletus</em>, Linnaeus 1758) collected from the Bay of Biscay stock (ICES areas 27.7.j,g & 27.8.a-c) in November 2020 (n=9), May 2021 (n=11), June 2021(n=7), July 2021 (n=15), September (n=15), October 2021 (n=3), November 2021 (n=27) and February 2022 (n=15).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 290 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 103 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li><strong>Histology_slides.zip:</strong> archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 264 histological slides acquired during this study. Data was split for smaller size downloads : <ul> <li><strong>Histology_slides_1of3 :</strong> histological sections for individuals numbered 062 to 094</li> <li><strong>Histology_slides_2of3 :</strong> histological sections for individuals numbered 100 to 250</li> <li><strong>Histology_slides_3of3 :</strong> histological sections for individuals numbered 290 to 304</li> </ul> </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip:</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration </strong>: Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used<strong>.</strong></li> <li><strong>Homogeneity</strong> : Reading results for 84 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 84 slides belong to 14 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total</strong> : Reading results for 103 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li><strong>Macro_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_MULL.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_MULL.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 103 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li><strong>Stereo_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_MULL.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_MULL.csv</strong> : a text data file (.csv) of the stereology count results of 173 slides read during this study. Among these slides, 84 were read to test the homogeneity distribution of different cell types found throughout each ovary (14 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 4 agents). Finally, 103 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul>
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