Find research datasets worth reusing

Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 5 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 5 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 3 FOV1

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 3 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 3 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 3 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV2

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

DUDE sample dataset

First sample training dataset such that challengers on DUDE (https://rrc.cvc.uab.es/?ch=23) can set up their systems.

Urbanus and Cosgrove Nature Communications (2023) - scRNAseq fastq files GFP positive sample in Figure 2

This dataset contains .fastq files for the GFP negative sample in the scRNAseq dataset used in Figures 2. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)

opencc-by-4.0Mar 2022View details →

Epidote - Sample 1 NanED, Data ESR1 & ESR2

The following submission contains the data collection frames for the sample Epidote under the NanEd round-robin project.  Precession Electron Diffraction (PED) was carried out on a single crystal. The datasets were processed with PETS2 software and refined kinematically and dynamically using Jana. The table below summarizes the data collection parameters for the data sets.   <table> <tbody> <tr> <td>General Information</td> <td> </td> </tr> <tr> <td>Project</td> <td>NanED (www.naned.eu)</td> </tr> <tr> <td>ESR Project</td> <td>ESR1 & ESR2</td> </tr> <tr> <td>Instrumental</td> <td> </td> </tr> <tr> <td>Instrument</td> <td>Zeiss Libra 120</td> </tr> <tr> <td>Radiation source</td> <td>LaB6</td> </tr> <tr> <td>Accelerating voltage</td> <td>120 kV</td> </tr> <tr> <td>Wavelength</td> <td>0.0335 Å</td> </tr> <tr> <td>Probe Type</td> <td>Nanodiffraction</td> </tr> <tr> <td>Beam Diameter</td> <td>150 nm</td> </tr> <tr> <td>Beam Convergence</td> <td>Parallel beam</td> </tr> <tr> <td>Detector</td> <td>Timepix Single Electron Detector</td> </tr> <tr> <td>Number of pixels in the image</td> <td>512 x 512</td> </tr> <tr> <td>Physical pixel size</td> <td>55 µm * 55 µm</td> </tr> <tr> <td>Effective camera length</td> <td>144 mm</td> </tr> <tr> <td>Calibration constant</td> <td>0.006027 Å-1/pixel</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td>Sample description</td> <td> </td> </tr> <tr> <td>Name</td> <td>Epidote</td> </tr> <tr> <td>Chemical composition</td> <td>Ca2FexAl3-xSi3O13H</td> </tr> <tr> <td>Sample source</td> <td>Natural source from Val d'Ossola, Italy</td> </tr> <tr> <td>Sample preparation</td> <td>Crushed with a mortar and diluted using isopropanol before depositing a drop on a Cu grid.</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td>Experimental</td> <td> </td> </tr> <tr> <td>Data type</td> <td>3D Electron Diffraction (3D-ED)</td> </tr> <tr> <td>Data collection method</td> <td>Precession</td> </tr> <tr> <td>Precession semiangle</td> <td>1º</td> </tr> <tr> <td>Temperature</td> <td>293 K</td> </tr> <tr> <td>Number of crystals contributing to the dataset</td> <td>1</td> </tr> <tr> <td>Number of experimental frames</td> <td>121</td> </tr> <tr> <td>Tilt range, tilt step</td> <td>+60º to -60º, -1º</td> </tr> <tr> <td>Exposure time per frame</td> <td>1000 ms</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td>Software</td> <td> </td> </tr> <tr> <td>Software used for data collection</td> <td>WinTEM (Zeiss), Digistar (NanoMEGAS) and Sophy (Software for Physics)</td> </tr> <tr> <td>Software used for processing</td> <td>PETS2</td> </tr> <tr> <td>Software used for solution</td> <td>Jana</td> </tr> <tr> <td>Software used for refinement</td> <td>Jana</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td>Authorship and bibliography</td> <td> </td> </tr> <tr> <td>Author(s) of the data</td> <td>Moussa Diame FAYE (ESR1) & Vincentia Emerson Agbemeh (ESR2)</td> </tr> <tr> <td>Related data</td> <td> </td> </tr> <tr> <td>Publication(s)</td> <td> </td> </tr> <tr> <td> </td> <td> </td> </tr> </tbody> </table>

Sample output from from Oktoberfest

Oktoberfest output for the 3 available tasks: 1- Spectral Library Generation 2- CE Calibration 3- Rescoring This is the result of running Oktoberfest with the input files available at <a href="https://zenodo.org/deposit/7600139">https://zenodo.org/deposit/7600139</a>

Database 1 - First-year samplings in the CodeRe-farm pilot sites in Greece

This database contains the results of the first year of AWIN assessments and general data recordings in the Greek goat pilots participating in the Code: Re-farm research project.

Sample data for JRC_seeker test run

This is sample data as part of the test run for JRC_seeker, a Snakemake pipeline for the genome-wide discovery of jointly regulated CpGs (JRCs) in pooled whole genome bisulfite sequencing (WGBS) data (https://github.com/BenjaminPlanterose/JRC_seeker).

DEcancer: Proteomics Datasets of Liquid Biopsy Samples

Two datasets used to build the DEcancer pipelines from open access data. The tables in the cancerseek folder are uploaded for convenience and can also be found from the supplementary data of <a href="https://doi.org/10.1016/j.isci.2019.04.035">Early Cancer Detection from Multianalyte Blood Test Results</a> paper. The nanoparticles dataset is processed with the Perseus software on the <a href="http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD017052">original data</a> from Blume et al's Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. These are distinct datasets in that the nanoparticles is a cohort of patients from a high dimensional, low sample size proteomics dataset.

A Spatial Analysis of Food Insecurity and Body Mass Index with Income and Grocery Store Density in a Diverse Sample of Adolescents and Young Adults

Abstract: Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minoritized populations. While there has been a proliferation of research linking food insecurity to obesity, these findings are mixed. It may be helpful to consider some additional geographic factors that may be associated with both factors including socioeconomic status and grocery store density. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults across two studies in a large, urban city. GIS analysis revealed that participants with the highest food insecurity (larger symbols) tend to live in the zip codes with the lowest median income. There did not appear to be clear a relationship between food insecurity and store density. Participants with the highest BMI tend to live in zip codes with lower median income and participants with higher BMI tended to live further away from downtown, which has the highest concentration of grocery stores in the city. Our findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence.

Natrolite - Sample 2 NanED Round Robin, Data: ESR12

Natrolite The following submission contains the data collection and processing of datasets for the sample epidote under the NanEd round-robin project.  A single crystal was identified, and precession data acquisition techniques were used to collect datasets on the same crystal.  All the data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder.     Precession: <table> <tbody> <tr> <td> General information: </td> <td>   </td> </tr> <tr> <td> Project </td> <td> NanED (www.naned.eu) </td> </tr> <tr> <td> ESR Project </td> <td> ESR12 - Round Robin </td> </tr> <tr> <td> Project Label </td> <td> RR1 </td> </tr> <tr> <td> Sample Label </td> <td> RR1-S2 </td> </tr> <tr> <td> Data set Label </td> <td> RR1-sample2-PEDT </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Instrumental: </td> <td>   </td> </tr> <tr> <td> Instrument </td> <td> Transmission electron microscope Jeol F200 </td> </tr> <tr> <td> Radiation source </td> <td> Cold FEG </td> </tr> <tr> <td> Accelerating voltage </td> <td> 200 kV </td> </tr> <tr> <td> Wavelength </td> <td> 0.0251 Å </td> </tr> <tr> <td> Probe Type </td> <td> Microdiffraction </td> </tr> <tr> <td> Beam Diameter </td> <td> 75nm </td> </tr> <tr> <td> Beam Convergence </td> <td> Parallel beam, convergence <0.1mrad </td> </tr> <tr> <td> Detector </td> <td> Hybrid pixel detector ASI Cheetah M3 (bottom mounted) </td> </tr> <tr> <td> Number of pixels in the image </td> <td> 512 x 512 </td> </tr> <tr> <td> Pixel size </td> <td> 55 µm x 55 µm </td> </tr> <tr> <td> Effective camera length </td> <td> 250 mm </td> </tr> <tr> <td> Calibration constant </td> <td> 0.005835 Å-1/pixel </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Sample description: </td> <td>   </td> </tr> <tr> <td> Name </td> <td> Natrolite </td> </tr> <tr> <td> Chemical composition </td> <td> Na2Si3Al2O10.(H2O)2 </td> </tr> <tr> <td> Sample source </td> <td> </td> </tr> <tr> <td> Sample preparation </td> <td> Powder crushed in a mortar and dispersed in n-butanol, drop deposited on a Cu grid with holey C film </td> </tr> <tr> <td> Experimental: </td> <td>   </td> </tr> <tr> <td> Data Type </td> <td> Electron diffraction data - 3D ED </td> </tr> <tr> <td> Data collection method </td> <td> Precession </td> </tr> <tr> <td> Temperature (K) used during data collection </td> <td> 293 K </td> </tr> <tr> <td> Number of crystals contributing to the data set </td> <td> 1 </td> </tr> <tr> <td> Number of experimental frames </td> <td> 109 </td> </tr> <tr> <td> tilt range, tilt step, tilt per frame </td> <td> -49° to +59°, 1°, 0° </td> </tr> <tr> <td> Precession angle </td> <td> 1.35° </td> </tr> <tr> <td> Exposure time per frame </td> <td> 500ms </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Software: </td> <td>   </td> </tr> <tr> <td> Software used for the data collection </td> <td> Instamatic software </td> </tr> <tr> <td> Software used for processing </td> <td> PETS2 (ver 2.1.20211012.1037) </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Authorship and bibliography </td> <td>   </td> </tr> <tr> <td> Author(s) of the data </td> <td> Sara Passuti (ESR12) </td> </tr> <tr> <td> Related data </td> <td>   </td> </tr> <tr> <td> Publication(s) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Files and data formats </td> <td>   </td> </tr> <tr> <td> Image folder </td> <td> tiff : Folder containing images of the diffraction pattern from each frames. </td> </tr> <tr> <td> Image format </td> <td> tiff_16bit </td> </tr> <tr> <td> Additional folders/files </td> <td> sample2_crystal_image : image of the crystal </td> </tr> <tr> <td>   </td> <td> sample2-exp3_petsdata : Log files of PETS2 processing </td> </tr> <tr> <td>   </td> <td> sample2-exp3.pts2 : input file for the program PETS2 used for processing  the data </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Notes: *Project Label "RR1" stands for Round Robin 1 *Data set label "RR1-sample2-PEDT" stands for Round Robin 1 sample 2, data collection method Precession. *The additional files can be found in the folder: Precession\data_collection_processing </td> </tr> </tbody> </table>

Epidote - Sample 1 NanED Round Robin, Data: ESR12

Epidote The following submission contains the data collection and processing of datasets for the sample epidote under the NanEd round-robin project.  A single crystal was identified, and precession data acquisition techniques were used to collect datasets on the same crystal. The data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder. Precession: <table> <tbody> <tr> <td> General information: </td> <td>   </td> </tr> <tr> <td> Project </td> <td> NanED (www.naned.eu) </td> </tr> <tr> <td> ESR Project </td> <td> ESR12 - Round Robin </td> </tr> <tr> <td> Project Label </td> <td> RR1 </td> </tr> <tr> <td> Sample Label </td> <td> RR1-S1 </td> </tr> <tr> <td> Data set Label </td> <td> RR1-sample1-PEDT </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Instrumental: </td> <td>   </td> </tr> <tr> <td> Instrument </td> <td> Transmission electron microscope Jeol F200 </td> </tr> <tr> <td> Radiation source </td> <td> Cold FEG </td> </tr> <tr> <td> Accelerating voltage </td> <td> 200 kV </td> </tr> <tr> <td> Wavelength </td> <td> 0.0251 Å </td> </tr> <tr> <td> Probe Type </td> <td> Microdiffraction </td> </tr> <tr> <td> Beam Diameter </td> <td> 75nm </td> </tr> <tr> <td> Beam Convergence </td> <td> Parallel beam, convergence <0.1mrad </td> </tr> <tr> <td> Detector </td> <td> Hybrid pixel detector ASI Cheetah M3 (bottom mounted) </td> </tr> <tr> <td> Number of pixels in the image </td> <td> 512 x 512 </td> </tr> <tr> <td> Pixel size </td> <td> 55 µm x 55 µm </td> </tr> <tr> <td> Effective camera length </td> <td> 200 mm </td> </tr> <tr> <td> Calibration constant </td> <td> 0.00720 Å-1/pixel </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Sample description: </td> <td>   </td> </tr> <tr> <td> Name </td> <td> Epidote </td> </tr> <tr> <td> Chemical composition </td> <td> Ca2FexAl3-xSi3O13H </td> </tr> <tr> <td> Sample source </td> <td> Natural source from Val d'Ossola, Italy </td> </tr> <tr> <td> Sample preparation </td> <td> Powder crushed on a mortar and dispersed in n-butanol, drop deposited on a Cu grid with holey C film </td> </tr> <tr> <td> Experimental: </td> <td>   </td> </tr> <tr> <td> Data Type </td> <td> Electron diffraction data - 3D ED </td> </tr> <tr> <td> Data collection method </td> <td> PEDT </td> </tr> <tr> <td> Temperature (K) used during data collection </td> <td> 293 K </td> </tr> <tr> <td> Number of crystals contributing to the data set </td> <td> 1 </td> </tr> <tr> <td> Number of experimental frames </td> <td> 95 </td> </tr> <tr> <td> tilt range, tilt step, tilt per frame </td> <td> -49.4° to +61.6°, 1°, 0° </td> </tr> <tr> <td> Precession angle </td> <td> 1.25° </td> </tr> <tr> <td> Exposure time per frame </td> <td> 500ms </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Software: </td> <td>   </td> </tr> <tr> <td> Software used for the data collection </td> <td> Instamatic software </td> </tr> <tr> <td> Software used for processing </td> <td> PETS2 (ver 2.1.20211012.1037) </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Authorship and bibliography </td> <td>   </td> </tr> <tr> <td> Author(s) of the data </td> <td> Sara Passuti (ESR12) </td> </tr> <tr> <td> Related data </td> <td>   </td> </tr> <tr> <td> Publication(s) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Files and data formats </td> <td>   </td> </tr> <tr> <td> Image folder </td> <td> tiff : Folder containing images of the diffraction pattern from each frames. </td> </tr> <tr> <td> Image format </td> <td> tiff_16bit </td> </tr> <tr> <td> Additional folders/files </td> <td> sample1_crystal_image : image of the crystal </td> </tr> <tr> <td>   </td> <td> sample1-exp4_petsdata : Log files of PETS2 processing </td> </tr> <tr> <td>   </td> <td> sample1-exp4.pts2 :_input file for the program PETS2 used for processing  the data </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Notes: *Project Label "RR1" stands for Round Robin 1 *Data set label "RR1-sample-PEDT" stands for Round Robin 1 sample 1, data collection method Precession. *The additional files can be found in the folder: Precession\Data_collection_processing </td> </tr> </tbody> </table>

Sample data for JRC_sorter test run

This is sample data as part of the test run for JRC_sorter, a classifier for Jointly Regulated CpGs (JRCs) (https://github.com/BenjaminPlanterose/JRC_sorter).

opencc-zeroFeb 2023View details →

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Data from: Flying high: Sampling savanna vegetation with UAV-lidar

The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs, or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimeters or less, which shows great promise for future comparison studies. We discuss how these results apply in practice, explaining the potential trade-offs of flying at higher altitudes and alternating flight pattern. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV-lidar sampling designs in savannas.

opencc-by-4.0Oct 2022View details →

Enantiopure Ibuprofen - Sample 3 NanED, Data ESR1 & ESR2

General Information   Project NanED (www.naned.eu) ESR Project ESR1 & ESR2 Instrumental   Instrument Zeiss Libra 120 Radiation source LaB6 Accelerating voltage 120 kV Wavelength 0.0335 Å Probe Type Nanodiffraction Beam Diameter 600 nm Beam Convergence Parallel beam Detector Timepix Single Electron Detector Number of pixels in the image 512 x 512 Physical pixel size 55 µm * 55 µm Effective camera length 180 mm Calibration constant 0.004815 Å-1/pixel     Sample description   Name Ibuprofen Chemical composition C13H18O2 Sample source Thermo Scientific (catalog. No: 333200050, lot: A0424385) Sample preparation Crushed between two glass slides     Experimental   Data type 3D Electron Diffraction (3D-ED) Data collection method Continuous Rotation Electron Diffraction (CRED) Rotation Speed 1.125 º/s Temperature 95.15 K Number of crystals contributing to the dataset 1 Number of experimental frames 226 Tilt range, tilt step -60º to +55, 0.5º Exposure time per frame 450 ms     Software   Software used for data collection WinTEM (Zeiss), Digistar (NanoMEGAS) and Sophy (Software for Physics) Software used for processing Pets2 Software used for solution Shelxt Software used for refinement Jana, Olex2     Authorship and bibliography   Author(s) of the data Moussa Diame FAYE (ESR1) & Vincentia Emerson Agbemeh (ESR2) Related data   Publication(s)         Precession: General Information   Project NanED (www.naned.eu) ESR Project ESR1 & ESR2 Instrumental   Instrument Zeiss Libra 120 Radiation source LaB6 Accelerating voltage 120 kV Wavelength 0.0335 Å Probe Type Nanodiffraction Beam Diameter 150 nm Beam Convergence Parallel beam Detector Timepix Single Electron Detector Number of pixels in the image 512 x 512 Physical pixel size 55 µm * 55 µm Effective camera length 180 mm Calibration constant 0.004815 Å-1/pixel     Sample description   Name Ibuprofen Chemical composition C13H18O2 Sample source Thermo Scientific (catalog. No: 333200050, lot: A0424385) Sample preparation Crushed between two glass slides     Experimental   Data type 3D Electron Diffraction (3D-ED) Data collection method Precession Precession Semiangle 1º   Temperature 95.15 K Number of crystals contributing to the dataset 1 Number of experimental frames 104 Tilt range, tilt step -60 to +55, 1º Exposure time per frame 1000 ms     Software   Software used for data collection WinTEM (Zeiss), Digistar (NanoMEGAS) and Sophy (Software for Physics) Software used for processing PETS2 Software used for solution Shelxt Software used for refinement Jana     Authorship and bibliography   Author(s) of the data Moussa Diame FAYE (ESR1) & Vincentia Emerson Agbemeh (ESR2) Related data   Publication(s)

Psychological Well-being, Food Insecurity, Academic Performance, and Other Risk Factors in a Sample of College Students in Jordan during Covid-19

This research investigated the occurrence of psychological well-being perception and its relationship with food insecurity, academic performance, and other correlates in a sample of university students in Amman, Jordan during Covid-19. A cross-sectional study was conducted in two phases. Phase-1 translated and validated an Arabic version of the Psychological General Wellbeing Index-Short version (PGWB-S) in 122 students from the University of Jordan. In Phase-2, 414 students completed demographic questionnaire, Arabic Versions of the PGWB-S, Ryff Psychological Wellbeing Scale, and Individual Food Insecurity Experience Scale. Ethical considerations: This manuscript has been read and approved by all authors. The authors confirm that there are no other persons, who satisfied the criteria for authorship, but are not listed. The order of authors listed in the manuscript has been approved by all of them. They also understand that the Corresponding Author is the sole contact for the Editorial process, and holds the responsibility for communicating with the other author about progress, submissions of revisions and final approval of proofs. Moreover, the authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. Furthermore, all data used in the study is confidential and the lead author has full access to the data reported in the manuscript. We confirm that there are no known conflicts of interest associated with this publication, which did not receive any financial support. Finally, the reporting of this work is compliant with The Code of Ethics of the World Medical Association (Declaration of Helsinki). In addition to that, the protocol of this research is approved by the Institutional Review Board at the University of Jordan, Amman, Jordan (ref no.: 2021-89).

Point sample database with spatial holdbacks for global forest edge model training

Global point dataset sampling 50 biophysical parameters to train the global forest carbon edge model at https://github.com/springinnovate/carbon_edge_model/releases/tag/1.2.0 Field schema: accessibility_to_cities_2015_30sec_compressed (Integer64) altitude_10sec_compressed (Integer64) baccini_carbon_data_2014_compressed (Integer64) bio_01_30sec_compressed (Real) bio_02_30sec_compressed (Real) bio_03_30sec_compressed (Real) bio_04_30sec_compressed (Real) bio_05_30sec_compressed (Real) bio_06_30sec_compressed (Real) bio_07_30sec_compressed (Real) bio_08_30sec_compressed (Real) bio_09_30sec_compressed (Real) bio_10_30sec_compressed (Real) bio_11_30sec_compressed (Real) bio_12_30sec_compressed (Real) bio_13_30sec_compressed (Real) bio_14_30sec_compressed (Real) bio_15_30sec_compressed (Real) bio_16_30sec_compressed (Real) bio_17_30sec_compressed (Real) bio_18_30sec_compressed (Real) bio_19_30sec_compressed (Real) cec_0-5cm_mean_compressed (Integer64) cec_5-15cm_mean_compressed (Integer64) cfvo_0-5cm_mean_compressed (Integer64) cfvo_5-15cm_mean_compressed (Integer64) clay_0-5cm_mean_compressed (Integer64) clay_5-15cm_mean_compressed (Integer64) fc_stack_hansen_forest_cover2014_compressed (Integer64) gf_0.4_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real) gf_1.45_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real) gf_5.0_fc_stack_hansen_forest_cover2014_compressed (Real) gf_5.0_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real) hillshade_10sec_compressed (Integer64) masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Integer64) night_lights_10sec_compressed (Real) night_lights_5min_compressed (Real) nitrogen_0-5cm_mean_compressed (Integer64) nitrogen_10sec_compressed (Integer64) nitrogen_5-15cm_mean_compressed (Integer64) phh2o_0-5cm_mean_compressed (Integer64) phh2o_5-15cm_mean_compressed (Integer64) sand_0-5cm_mean_compressed (Integer64) silt_0-5cm_mean_compressed (Integer64) silt_5-15cm_mean_compressed (Integer64) slope_10sec_compressed (Real) soc_0-5cm_mean_compressed (Integer64) soc_5-15cm_mean_compressed (Integer64) tri_10sec_compressed (Real) wind_speed_10sec_compressed (Real)

Supporting publication for 'Prevalence sample-based guidance for reporting 2022 data'

The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use. The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.