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.
1,803
datasets available to search
ShareScore release 0.9.0
Dataset results
1,803 results for “Producers”
Primary producer biomarker profiles of bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) collected from the Beaufort Sea coastal lagoons,2021-2024
Within Stefansson Sound in Prudhoe Bay, AK various organic matter sources were collected to determine multiple biomarker baseline profiles (i.e., bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA), fatty acids (FA)). Some organic matter sources were collected from Elson lagoon in Utqiaġvik, AK and Kaktovik and Jago lagoons in Kaktovik, AK to supplement low sample sizes in some organic matter source groups. Kelp, red algae, terrestrial plants, phytoplankton, and ice algae were collected in 2024 with some supplement samples collected in 2021 - 2023. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 23 fatty acids: C11:0, C12:0, C14:0, C15:1, C15:0, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:2n6 cis, C18:1n7, C18:3n3, C20:0, C18:3n6, C20:4n6, C21:0, C22:0, C22:1n9, C23:0, C24:0, C22:6n3. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe). Additionally, we used ice algal diatoms collected in the Arctic (landfast ice near Utqiaġvik, Alaska) and cultured in a laboratory setting at the University of Alaska Fairbanks to compare the CSIA-EAA fingerprints of field (composites) ice algal samples and isolate diatoms samples.
Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities
<p>Example <strong>Microscopy Metadata </strong>(Microscope.JSON and Settings.JSON)<strong> files </strong>produced using<strong> <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> </strong>to document the <strong>Hardware Specifications</strong> of example Microscopes and the <strong>Image Acquisition Settings</strong> utilized to acquire example images as listed in the table below.</p> <blockquote> <p>For each facility, the dataset contains two JSON files:</p> <ol> <li><strong>Microscope.JSON file</strong> (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)</li> <li><strong>Settings.JSON file</strong> (indicated with the name of the image and with the _AS suffix)</li> </ol> </blockquote> <p><strong>Micro-Meta App was</strong> developed as part of a <strong>global community initiative</strong> including the <a href="http://www.4dnucleome.org/"><strong>4D Nucleome (4DN)</strong> </a>Imaging Working Group, <strong>BioImaging North America (BINA)</strong> <a href="https://www.bioimagingna.org/qc-dm-wg">Quality Control and Data Management Working Group</a>, and <strong>QUAlity and REProducibility for Instrument and Images in Light Microscopy</strong> (<a href="https://quarep.org/"><strong>QUAREP-LiMi</strong></a>), to extend the <strong>Open Microscopy Environment (OME)</strong> <a href="https://www.openmicroscopy.org/Schemas/Documentation/Generated/OME-2016-06/ome.html">data model</a>.</p> <blockquote> <p>The works of this <strong>global community effort</strong> resulted in multiple publications featured on a recent <strong>Nature Methods FOCUS ISSUE </strong>dedicated to <a href="https://www.nature.com/collections/djiciihhjh">Reporting and reproducibility in microscopy</a>.</p> </blockquote> <blockquote> <p><strong>Learn More!</strong> For a thorough description of <strong>Micro-Meta App</strong> consult our recent <a href="https://doi.org/10.1038/s41592-021-01315-z">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.05.31.446382">BioRxiv.org</a> publications!</p> </blockquote> <p> </p> <table> <tbody> <tr> <td><strong>Nr.</strong></td> <td><strong>Manufacturer</strong></td> <td><strong>Model</strong></td> <td><strong>Tier</strong></td> <td><strong>Εxperiment Type</strong></td> <td><strong>Facility Name</strong></td> <td><strong>Department and Institution</strong></td> <td><strong>URL</strong></td> <td><strong>References</strong></td> </tr> <tr> <td>1</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with LSM 710 scan head)</strong></td> <td>1</td> <td>3D visualization of superhydrophobic polymer-nanoparticles</td> <td>Centre for Cell Imaging (CCI)</td> <td>University of Liverpool</td> <td>https://cci.liv.ac.uk/equipment_710.html</td> <td>Upton et al., 2020</td> </tr> <tr> <td>2</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer (Axiovert 200M)</strong></td> <td>2</td> <td>Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP</td> <td>Advanced BioImaging Facility (ABIF).</td> <td>McGill University</td> <td>https://www.mcgill.ca/abif/equipment/axiovert-1</td> <td>Kiepas et al., 2020</td> </tr> <tr> <td>3</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with Spinning Disk)</strong></td> <td>2</td> <td>Immunofluorescence imaging of cryosection of Mouse kidney</td> <td>Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/)</td> <td>Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal</td> <td>https://www.chumontreal.qc.ca/crchum/plateformes-et-services (the web site is for all core facilities, not specifically for the core facility hosting this microscope)</td> <td>Pilliod et al., 2020</td> </tr> <tr> <td>4</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Imager Z2 (with Apotome)</strong></td> <td>2</td> <td>Immunofluorescence imaging of mitotic division in Hela cells using </td> <td>Bioimaging Unit</td> <td>Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/</td> <td>Watson et al., 2020</td> </tr> <tr> <td>5</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1</strong></td> <td>2</td> <td>Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients.</td> <td>Life Imaging Center (LIC)</td> <td>Centre for Integrative Signalling Analysis (CISA), University of Freiburg</td> <td>https://miap.eu/equipments/sd-i-abl/</td> <td>Hannibal et al., 2020</td> </tr> <tr> <td>6</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI6000B</strong></td> <td>2</td> <td>3D immunofluorescence imaging rhinovirus infected macrophages </td> <td>IMAG'IC Confocal Microscopy Facility</td> <td>Institut Cochin, CNRS, INSERM, Université de Paris</td> <td>https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view</td> <td>Jubrail et al., 2020</td> </tr> <tr> <td>7</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DM5500B</strong></td> <td>2</td> <td>Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks</td> <td>Bioimaging Unit</td> <td>Edwardson Building on the Campus for Ageing and Vitality, Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview</td> <td>da Silva et al., 2019; Nelson et al., 2012<br> </td> </tr> <tr> <td>8</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI8-CS (with TCS SP8 STED 3X)</strong></td> <td>2</td> <td>Live-cell imaging of N. benthamiana leaves cells-derived protoplasts</td> <td>Center for Advanced Imaging (CAi)</td> <td>School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf</td> <td>https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x</td> <td>Singer et al., 2017; Hänsch et al., 2020</td> </tr> <tr> <td>9</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti</strong></td> <td>2</td> <td>Immunofluorescence analysis of the cytoskeleton structure in COS cells</td> <td>Advanced Imaging Center (AIC)</td> <td>Janelia Research Campus, Howard Hughes Medical Institute</td> <td>https://www.janelia.org/support-team/light-microscopy/equipment</td> <td>Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020</td> </tr> <tr> <td>10</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti-E (HCA)</strong></td> <td>2</td> <td>Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies</td> <td>Light Microscopy Facility (IALS-LIF)</td> <td>Institute for Applied Life Sciences, University of Massachusetts at Amherst</td> <td>https://www.umass.edu/ials/light-microscopy</td> <td>Fernandez et al., 2020</td> </tr> <tr> <td>11</td> <td><strong>Nikon Instruments/Coleman laboratory (customized)</strong></td> <td><strong>TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti</strong></td> <td>2</td> <td>Single-particle tracking of Halo-tagged PCNA in Lox cells</td> <td>Coleman laboratory</td> <td>Anatomy and Structural Biology Department, The Albert Einstein College of Medicine</td> <td>https://einsteinmed.org/faculty/12252/robert-coleman/</td> <td>Drosopoulos et al., 2020</td> </tr> <tr> <td>12</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti (with Andor Dragon Fly Spinning Disk)</strong></td> <td>2</td> <td>Investigation of the 3D structure of cerebral organoids</td> <td>Montpellier Resources Imagerie</td> <td>Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier</td> <td>https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html</td> <td>Ayala-Nunez et al., 2019</td> </tr> <tr> <td>13</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium </td> <td>Neuroscience Center Microscopy Core</td> <td>Neuroscience Center, University of North Carolina</td> <td>https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/</td> <td>Aghajanian et al., 2021</td> </tr> <tr> <td>14</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Live-cell imaging of bacterial cells expressing GFP-PopZ</td> <td>Microscopy Resources on the North Quad (MicRoN)</td> <td>Harvard Medical School </td> <td>https://micron.hms.harvard.edu/</td> <td>Lim and Bernhardt 2019; Lim et al., 2019</td> </tr> <tr> <td>15</td> <td><strong>Olympus/Biomedical Imaging Group (customized)</strong></td> <td><strong>TIRF Epifluorescence Structured light Microscope (TESM)/IX71</strong></td> <td>3</td> <td>3D distribution of HIV-1 in the nucleus of human cells</td> <td>Biomedical Imaging Group</td> <td>Program in Molecular Medicine, University of Massachusetts Medical School</td> <td>https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope</td> <td>Navaroli et al., 2012</td> </tr> <tr> <td>16</td> <td><strong>Olympus/Computer Vision Laboratory (customized)</strong></td> <td><strong>3D BrightField Scanner/IX71</strong></td> <td>3</td> <td>Transmitted light brightfield visualization of swimming spermatocytes</td> <td>Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology</td> <td>Universidad Nacional Autonoma de Mexico (UNAM)</td> <td>https://lnma.unam.mx/wp/</td> <td>Pimentel et al., 2012; Silva-Villalobos et al., 2014</td> </tr> </tbody> </table> <p><strong>Getting started</strong></p> <p>Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files:</p> <ol> <li><a href="https://vimeo.com/562022222">Video 1</a></li> <li><a href="https://vimeo.com/562022281">Video 2</a></li> </ol> <p><strong>More information</strong></p> <blockquote> <p>For full information on how to use Micro-Meta App please utilize the following resources:</p> <ol> <li>Micro-Meta App <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">website</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/index.html">Full documentation</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/intro/installation.html">Installation</a> instructions</li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/index.html#step-by-step-instructions">Step-by-Step Instructions</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/VideoTutorials.html#micro-meta-app-video-tutorials">Tutorial Videos</a></li> </ol> </blockquote> <p><strong>Background</strong></p> <p>If you want to learn more about the importance of <strong>metadata and quality contro</strong>l to ensure full <strong>reproducibility, quality and scientific value</strong> in light microscopy, please take a look at our recent publications describing the development of community-driven light <strong>4DN-BINA-OME Microscopy Metadata</strong> specifications <a href="https://doi.org/10.1038/s41592-021-01327-9">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.04.25.441198">BioRxiv.org</a> and our <a href="https://arxiv.org/abs/1910.11370">overview manuscript</a> entitled <strong>A perspective on Microscopy Metadata: data provenance and quality control</strong>.</p> <p> </p> <p> </p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Stable isotope values of consumers, producers, and organic matter in the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2019 – ongoing
Wetland food webs have often been characterized as detrital-based ‘brown’ energy pyramids, whereas the relative role of autotrophic (‘green’) vs. microbial (‘brown’) energy sources falls along a continuum set by physical drivers, as well as autochthonous and allochthonous inputs (Moore et al. 2004; Evans-White & Halvorson 2017) that change with ecosystem development (Schmitz et al. 2006). In the Florida Coastal Everglades (FCE), metabolic imbalances, including the collapse of calcareous periphyton mats, begin with a loss of foundation species primary production and legacy organic matter (Gaiser et al. 2006). This process likely enhances heterotrophic microbial productivity (Schulte 2016) and the supply of detrital energy to consumers by changing bioavailable and recalcitrant carbon supplies (Baggett et al. 2013). A shift from complex periphyton communities to transient planktonic communities under elevated P exposure reduces habitat structure and animal refuges but increases ‘green’ energy supplies and edibility (Trexler et al. 2015; Naja et al. 2017). Multiple sites (n=9) within the FCE were selected to document changes in coastal food webs as a result of eutrophication and increasing hydrologic variability. The project began in 2019 and is currently ongoing. References: Baggett, L. P., Heck, K. L., Frankovich, T. A., Armitage, A. R., & Fourqurean, J. W. (2013). Stoichiometry, growth, and fecundity responses to nutrient enrichment by invertebrate grazers in sub-tropical turtle grass (Thalassia testudinum) meadows. Marine biology, 160, 169-180. Evans-White, M. A., and H. M. Halvorson. 2017. Comparing the Ecological Stoichiometry in Green and Brown Food Webs – A Review and Meta-analysis of Freshwater Food Webs. Frontiers in Microbiology 8:1184. Gaiser, E. E., Childers, D. L., Jones, R. D., Richards, J. H., Scinto, L. J., & Trexler, J. C. (2006). Periphyton responses to eutrophication in the Florida Everglades: cross‐system patterns of structural and compositional cha
Data for the extraction of the critical temperature of niobium films deposited on copper produced at CERN for SRF applications
<p>Each data set contains the voltage signal amplitude induced in a pickup coil by an alternating magnetic field, crossing the niobium-film-on-copper sample during its state transition from normal conducting to superconducting, the corresponding sample temperature and the estimated errors for the two measured quantities. File columns: #1 sample temperature in Kelvin, #2 estimated temperature error, #3 voltage amplitude in pickup coil in Volt, #4 estimated amplitude error.</p> <p>One data set corresponds to one sample. The critical temperature can be extracted as fit parameter by fitting the amplitude versus temperature data with a logistic function, where it corresponds to the half height of the state transition curve.</p>
Catalysis of Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by D. andersoni (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the chromogenic substrate Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by <em>Dermacentor andersoni</em>, for fixed concentration of enzyme (and modulator) and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and <em>PNAS 116 (28) 13873-13878</em> (<a href="https://doi.org/10.1073/pnas.1905177116">https://doi.org/10.1073/pnas.1905177116</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Tos-Gly-Pro-Arg-p-nitroanilide in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (0.15 nM)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
Resilience of pig producers in Europe
<p>This dataset collected for the organic core POWER project to assess resilience capacities of organic pig prodcuers in Austria, Danemark, Italy, Sweden and Switzerland. These datasets have been anonymized. </p> <p> </p> <p>The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely</p> <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>breeding type</td> <td>type of pig entreprise on the found on the farm</td> <td>breeding, finishing or both</td> </tr> <tr> <td>entrerprise_x</td> <td>description of other entreprises found on the farm</td> <td>feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing.</td> </tr> <tr> <td>number non-pig entreprise</td> <td>number of entreprise describes</td> <td>integer</td> </tr> <tr> <td>structure</td> <td>type of pig housing structure</td> <td>permanent, temporary, both</td> </tr> <tr> <td>outdoor area</td> <td>type of oudoor access for pig</td> <td>concrete, shifting arable land, permanent pasture</td> </tr> <tr> <td>LSU</td> <td>livstock standard units computed following Eurostat standards</td> <td>numeric</td> </tr> <tr> <td>pig/ha</td> <td>intensity of production as LSU/UAApig</td> <td>numeric</td> </tr> <tr> <td>self-sufficiency</td> <td>percentage of pig feed produced on farm</td> <td>numeric</td> </tr> <tr> <td>UAA pig</td> <td>utilized agricultural area for the pig production</td> <td>numeric</td> </tr> <tr> <td>UAA total</td> <td>utilized agricultural area of the farm</td> <td>numeric</td> </tr> </tbody> </table> <p> </p> <p>The resilience data is the result of the interpretation of farmers' resilience narratives, which were interpreted been interpreted using the Meuwissen et al, 2019 farming systems framework. The data is in long fromat and represents a particular resilience capacity related to a specific shock. More particularly, the data contains the follwing information</p> <p> </p> <p> </p> <table> <tbody> <tr> <td>variables name</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>country</td> <td>country code</td> <td>based on ISO2</td> </tr> <tr> <td>question related to shocks</td> <td>shocks to which the resilience narrative related to</td> <td>input cost, price, outbreak, climate, legislation, labour, general</td> </tr> <tr> <td>narratives (a= first, b=second)</td> <td>identifier of the narrative within a question</td> <td>a, b</td> </tr> <tr> <td>capacity</td> <td>resilience capacity following the Meuwissen et al (2019) framework</td> <td>robustness, adaptability, transformability, non-resilience</td> </tr> <tr> <td>resilience attribute type</td> <td>resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper)</td> <td> <p>functional diversity, response diversity, modularity, tighness of feedback, social capital, attitude, system reserve (physical captial -inherent), system reserve (physical capital -use), system reserve (natural capital -inherent) system reserve (human capital - use)</p> </td> </tr> <tr> <td>resilience attribute</td> <td>description of the attribute that led to the resilience attribute type classification</td> <td> <p>ability to convert to cash crop, ability to offer good working conditions, ability to switch brand, access to financial services, access to technical solutions, adapted crops, adding finishing section, adjust feed production, adjust volume of pig production, adjusting paddock size to enable double fencing, advisory and veterinary services, believe in organic, brand building with social media, build temporary shelter, build up savings, by-product through partnership, capacity to access more land, change external feed, change feed ratio, conservable end product, create microclimates, create new brand, created a young farmer network, customer relation, decrease pig, decrease pig production, direct marketing, diverse farm, diverse sale channels, do something else, double fencing, efficiency, entrepreneurship, excess cereal production, exploring governance model as no successor, family labour, farmer owned value chain, fencing, financial lock-in, flexible infrastructure (enabling), flexible pig keeping system, forest system, good indoor infrastructure, good infrastructure, good relation to customers, governmental support, habit, has margin, home feed production, inadequate salary, increase cash crop, increase own work, increase own working time, independent feed ratio, indoor keeping, indoor production, innovator, innovator (one welfare) , insurance, margins, mechanisation, mobile mode of production, neighbor network, neighborhood early warning, neighborhood network, new cooling infrastructure, niche production, no competition, no fencing option, no own farm, land or infrastructure, no qualified staff required, offering jobs to young people, other livestock, part time worker, partnership with other farmers, producing more home grown feed, profit, reduced pig production, rely on sectoral organization, resistant breed, robust animals, robust breed, sectoral power, sectoral response, short term feed contracts, social media, soil health, split production on other farms, staffing agency (through advisory services), sufficient outdoor space, sufficient pasture, sufficient space, sufficient space (enabling), switch to indoor production, switch to other livestock, tiredness in the sector, Too big to fail, training, unique pig keeping system, up-to-date infrastructure, volunteer networks, wallow, work with nature</p> </td> </tr> </tbody> </table> <p> </p> <p>To compute the resilience capacity score (<em>Cscore)</em></p> <p>assign 0 to lack of resilience, 1 to robustness, 2 to adpababilty and 3 to transformability. If there is more than one narrative with a different capacity, the average score between mentionned capacities was taken.</p> <p>Use following R code in dplyr</p> <p><em>mydata<-</em> ResilienceDataPreProcessed<em> %>% </em></p> <p><em> mutate(code = ifelse(capacity=="robustness", 1,ifelse(capacity=="adaptability",10,ifelse(capacity=="transformability",100,ifelse(capacity =="no resilience capacity",1000,ifelse(capacity=="no long term resilience capacity",10000,ifelse(capacity=="no short term resilience",1000,NA)))))))%>% </em></p> <p><em> group_by(farm, question)%>% </em></p> <p><em> summarise(Ccode=sum(code))%>% </em></p> <p><em> mutate(Cscore=ifelse(Ccode==1|Ccode==2| Ccode==3, 1,ifelse(Ccode==20|Ccode==10|Ccode==111,2, ifelse(Ccode==100|Ccode==200,3, ifelse(Ccode==11|Ccode==21, 1.5,ifelse(Ccode==110|Ccode==120, 2.5,ifelse(Ccode==101,3,ifelse(Ccode==1000,0,ifelse(Ccode==10001|Ccode==1001,0.5,NA)) ))) ))))</em></p> <p> </p> <p><strong>Resilience questionnaire</strong></p> <p><strong>Farm number:</strong></p> <p><strong>Farm name or ID:</strong></p> <p><strong>Country:</strong></p> <p> </p> <p><strong>System descriptors</strong></p> <table> <tbody> <tr> <td> <p>Breeding or finishing (or both)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Indoor or outdoor (or a mix)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Organic or conventional</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Number of years organic</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>1) Has your farm experienced significant challenges in the last 5 years?</strong></p> <table> <tbody> <tr> <td> <p>Yes or no?</p> </td> <td> <p>Yes / No</p> </td> </tr> <tr> <td> <p>If "no", what factars (farm/external) created this resilience?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>If "yes", please describe the 1st challenge</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>Did this change your management or farm structure subsequently (and how)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>If "yes", please describe a 2nd challenge</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>Did this change your management or farm structure subsequently (and how)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>2) In the future, how do you feel your pig system would cope with these challenges:</strong></p> <p>a) Decreasing or negative margins due to increased feed or other input costs?</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>b). Decreasing or negative margins due to reduced pig prices?</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p>c) Wide spread disease outbreak such as African Swine Fever</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>d) Climate change impact, e.g. severe storms, flooding, drought, hot seasons</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p>e) Changing legislation impact, e.g. increased floor space allowance indoors, mandatory access to pasture, more land required (lower stocking densities to reduce nutrient loads from pasture systems or in general for the whole farm)</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>f) Shortage in qualified ‘work-mass’ (difficulties in recruiting qualified employees)</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p><strong>3) Any other comments on resilience of their system?</strong></p> <p><strong>4) General comments/system description?</strong></p>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 1
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the first subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 2
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Cyprus (2021-2022) - Choirokoitia region
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Cypriotic territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over one of the two determined locations in Cyprus (e.g. Choirokoitia). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 29/3/2021- 1/4/2021</li> <li><strong>2nd flight:</strong> 30/8/2021-2/9/2021</li> <li><strong>3rd flight:</strong> 29/11/2021-03/12/2021</li> <li><strong>4th flight:</strong> 17-20/5/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Cyprus (2021-2022) - Akaki region
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Cypriotic territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over one of the two determined locations in Cyprus (e.g. Akaki). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 29/3/2021- 1/4/2021</li> <li><strong>2nd flight:</strong> 30/8/2021-2/9/2021</li> <li><strong>3rd flight:</strong> 29/11/2021-03/12/2021</li> <li><strong>4th flight:</strong> 17-20/5/2022</li> </ul>
VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 2
<p>In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements.</p> <p>The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates: </p> <ul> <li><strong>1st flight:</strong> 24-27/5/2021</li> <li><strong>2nd flight:</strong> 26-29/7/2021</li> <li><strong>3rd flight:</strong> 13-16/9/2021</li> <li><strong>4th flight:</strong> 25-28/8/2022</li> </ul>
Dataset of regional NRSS available for producing BBFs in the EU
<p>The data files contain information regarding the calculation of nutrient rich side stream (NRSS) quantities and their nutrient contents. The data is produced within WP1 of LEX4BIO project.</p> <p>The full description of data sources and calculations is available: https://lex4bio.eu/wp-content/uploads/2022/09/LEX4BIO_D1.1_WP1-1.pdf</p> <p>Data summaries are available: https://px.luke.fi/PxWeb/pxweb/en/maatalous/maatalous__biomassa/</p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Image segmentations produced by BAMF under the AIMI Annotations initiative
<p>The Imaging Data Commons (IDC)(<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) [1] connects researchers with publicly available cancer imaging data, often linked with other types of cancer data. Many of the collections have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provide an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnUNet [2] based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections.</p> <p>To validate the model's performance, roughly 10% of the AI predictions were assigned to a validation set. For this set, a board-certified radiologist graded the quality of AI predictions on a Likert scale. If they did not 'strongly agree' with the AI output, the reviewer corrected the segmentation. </p> <p>This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images.</p> <p><em>Only 10% of the AI-derived annotations provided in this dataset are verified by expert radiologists . More details, on model training and annotations are provided within the associated manuscript to ensure transparency and reproducibility.</em></p> <p> </p> <p>This work was done in two stages. Versions 1.x of this record were from the first stage. Versions 2.x added additional records. In the Version 1.x collections, a medical student (non-expert) reviewed all the AI predictions and rated them on a 5-point Likert Scale, for any AI predictions in the validation set that they did not 'strongly agree' with, the non-expert provided corrected segmentations. This non-expert was not utilized for the Version 2.x additional records.</p> <p> </p> <h3>Likert Score Definition:</h3> <p>Guidelines for reviewers to grade the quality of AI segmentations.</p> <ul> <li>5 Strongly Agree - Use-as-is (i.e., clinically acceptable, and could be used for treatment without change)</li> <li>4 Agree - Minor edits that are not necessary. Stylistic differences, but not clinically important. The current segmentation is acceptable</li> <li>3 Neither agree nor disagree - Minor edits that are necessary. Minor edits are those that the review judges can be made in less time than starting from scratch or are expected to have minimal effect on treatment outcome</li> <li>2 Disagree - Major edits. This category indicates that the necessary edit is required to ensure correctness, and sufficiently significant that user would prefer to start from the scratch</li> <li>1 Strongly disagree - Unusable. This category indicates that the quality of the automatic annotations is so bad that they are unusable.</li> </ul> <p> </p> <h3>Zip File Folder Structure</h3> <p>Each zip file in the collection correlates to a specific segmentation task. The common folder structure is</p> <ul> <li><em>ai-segmentations-dcm </em>This directory contains the AI model predictions in DICOM-SEG format for all analyzed IDC collection files</li> <li><em>qa-segmentations-dcm </em>This directory contains manual corrected segmentation files, based on the AI prediction, in DICOM-SEG format. Only a fraction, ~10%, of the AI predictions were corrected. Corrections were performed by radiologist (rad*) and non-experts (ne*)</li> <li><em>qa-results.csv</em> CSV file linking the study/series UIDs with the ai segmentation file, radiologist corrected segmentation file, radiologist ratings of AI performance.</li> </ul> <p> </p> <h3><strong><em>qa-results.csv Columns</em></strong></h3> <p>The qa-results.csv file contains metadata about the segmentations, their related IDC case image, as well as the Likert ratings and comments by the reviewers.</p> <div> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p><em>Collection</em></p> </td> <td> <p>The name of the IDC collection for this case</p> </td> </tr> <tr> <td> <p><em>PatientID</em></p> </td> <td> <p>PatientID in DICOM metadata of scan. Also called Case ID in the IDC</p> </td> </tr> <tr> <td> <p><em>StudyInstanceUID</em></p> </td> <td> <p>StudyInstanceUID in the DICOM metadata of the scan</p> </td> </tr> <tr> <td> <p><em>SeriesInstanceUID</em></p> </td> <td> <p>SeriesInstanceUID in the DICOM metadata of the scan</p> </td> </tr> <tr> <td> <p><em>Validation</em></p> </td> <td> <p>true/false if this scan was manually reviewed</p> </td> </tr> <tr> <td> <p><em>Reviewer</em></p> </td> <td> <p>Coded ID of the reviewer. Radiologist IDs start with ‘rad’ non-expect IDs start with ‘ne’</p> </td> </tr> <tr> <td> <p><em>AimiProjectYear</em></p> </td> <td> <p>2023 or 2024, This work was split over two years. The main methodology difference between the two is that in 2023, a non-expert also reviewed the AI output, but a non-expert was not utilized in 2024.</p> </td> </tr> <tr> <td> <p><em>AISegmentation</em></p> </td> <td> <p>The filename of the AI prediction file in DICOM-seg format. This file is in the ai-segmentations-dcm folder.</p> </td> </tr> <tr> <td> <p><em>CorrectedSegmentation</em></p> </td> <td> <p>The filename of the reviewer-corrected prediction file in DICOM-seg format. This file is in the qa-segmentations-dcm folder. If the reviewer strongly agreed with the AI for all segments, they did not provide any correction file.</p> </td> </tr> <tr> <td> <p><em>Was the AI predicted ROIs accurate?</em></p> </td> <td> <p>This column appears one for each segment in the task for images from AimiProjectYear 2023. The reviewer rates segmentation quality on a Likert scale. In tasks that have multiple labels in the output, there is only one rating to cover them all.</p> </td> </tr> <tr> <td> <p><em>Was the AI predicted {SEGMENT_NAME} label accurate?</em></p> <p><em><strong> </strong></em></p> </td> <td> <p>This column appears one for each segment in the task for images from AimiProjectYear 2024. The reviewer rates each segment for its quality on a Likert scale.</p> </td> </tr> <tr> <td> <p><em>Do you have any comments about the AI predicted ROIs?</em></p> <p><em><strong> </strong></em></p> </td> <td> <p>Open ended question for the reviewer</p> </td> </tr> <tr> <td> <p><em>Do you have any comments about the findings from the study scans?</em></p> </td> <td> <p>Open ended question for the reviewer</p> </td> </tr> </tbody> </table> </div> <p> </p> <h3>File Overview</h3> <h4>brain-mr.zip</h4> <ul> <li>Segment Description: brain tumor regions: necrosis, edema, enhancing</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/upenn-gbm/">UPENN-GBM</a></li> <li>Links: <a href="../records/11582627">model weights</a>, <a href="https://github.com/bamf-health/aimi-brain-mr">github</a></li> </ul> <h4>breast-fdg-pet-ct.zip</h4> <ul> <li>Segment Description: FDG-avid lesions in breast from FDG PET/CT scans QIN-Breast</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/qin-breast/">QIN-Breast</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290054">model weights, </a><a href="https://github.com/bamf-health/aimi-breast-pet-ct">github</a></li> </ul> <h4>breast-mr.zip</h4> <ul> <li>Segment Description: Breast, Fibroglandular tissue, structural tumor</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/duke-breast-cancer-mri/">duke-breast-cancer-mri</a></li> <li>Links: <a href="../records/11998679">model weights</a>, <a href="https://github.com/bamf-health/aimi-breast-mr">github</a></li> </ul> <h4>kidney-ct.zip</h4> <ul> <li>Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans</li> <li>IDS Collection: <a href="https://www.cancerimagingarchive.net/collection/tcga-kirc/">TCGA-KIRC,</a> <a href="https://www.cancerimagingarchive.net/collection/tcga-kirp/">TCGA-KIRP</a>, <a href="https://www.cancerimagingarchive.net/collection/tcga-kich/">TCGA-KICH</a>, <a href="https://www.cancerimagingarchive.net/collection/cptac-ccrcc/">CPTAC-CCRCC</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8277845">model weights, </a><a href="https://github.com/bamf-health/aimi-kidney-ct">github</a></li> </ul> <h4>liver-ct.zip</h4> <ul> <li>Segment Description: Liver from CT scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/TCGA-LIHC/">TCGA-LIHC</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8270230">model weights, </a><a href="https://github.com/bamf-health/aimi-liver-ct">github</a></li> </ul> <h4>liver2-ct.zip</h4> <ul> <li>Segment Description: Liver and Lesions from CT scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/hcc-tace-seg/">HCC-TACE-SEG</a>, <a href="https://www.cancerimagingarchive.net/collection/colorectal-liver-metastases/">COLORECTAL-LIVER-METASTASES</a></li> <li>Links: <a href="../records/11582728">model weights</a>, <a href="https://github.com/bamf-health/aimi-liver-tumor-ct">github</a></li> </ul> <h4>liver-mr.zip</h4> <ul> <li>Segment Description: Liver from T1 MRI scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/TCGA-LIHC/">TCGA-LIHC</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290123">model weights, </a><a href="https://github.com/bamf-health/aimi-liver-mr">github</a></li> </ul> <h4>lung-ct.zip</h4> <ul> <li>Segment Description: Lung and Nodules (3mm-30mm) from CT scans</li> <li>IDC Collections:<br> <ul> <li><a href="https://www.cancerimagingarchive.net/collection/anti-pd-1_lung/">Anti-PD-1-Lung</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/lung-pet-ct-dx/">LUNG-PET-CT-Dx</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/">NSCLC Radiogenomics</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/rider-lung-pet-ct/">RIDER Lung PET-CT</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUAD/">TCGA-LUAD</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUSC/">TCGA-LUSC</a></li> </ul> </li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290146">model weights 1, </a><a href="../record/8290169">model weights 2, </a><a href="https://github.com/bamf-health/aimi-lung-ct">github</a></li> </ul> <h4>lung2-ct.zip</h4> <ul> <li>Improved model version</li> <li>Segment Description: Lung and Nodules (3mm-30mm) from CT scans</li> <li>IDC Collections:<br> <ul> <li><a href="https://www.cancerimagingarchive.net/collection/QIN-LUNG-CT">QIN-LUNG-CT</a>, <a href="https://www.cancerimagingarchive.net/collection/spie-aapm-lung-ct-challenge/">SPIE-AAPM Lung CT Challenge</a></li> </ul> </li> <li>Links: <a href="../records/11582738">model weights</a>, <a href="https://github.com/bamf-health/aimi-lung2-ct">github</a></li> </ul> <h4>lung-fdg-pet-ct.zip</h4> <ul> <li>Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans</li> <li>IDC Collections: <ul> <li><a href="https://www.cancerimagingarchive.net/collection/acrin-nsclc-fdg-pet/">ACRIN-NSCLC-FDG-PET</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/anti-pd-1_lung/">Anti-PD-1-Lung</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/lung-pet-ct-dx/">LUNG-PET-CT-Dx</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/">NSCLC Radiogenomics</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/rider-lung-pet-ct/">RIDER Lung PET-CT</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUAD/">TCGA-LUAD</a></li> <li><a href="https://www.cancerimagingarchive.net/collection/TCGA-LUSC/">TCGA-LUSC</a></li> </ul> </li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290054">model weights, </a><a href="https://github.com/bamf-health/aimi-lung-pet-ct">github</a></li> </ul> <h4>prostate-mr.zip</h4> <ul> <li>Segment Description: Prostate from T2 MRI scans</li> <li>IDC Collection: <a href="https://www.cancerimagingarchive.net/collection/ProstateX/">ProstateX,</a> <a href="https://www.cancerimagingarchive.net/collection/prostate-mri-us-biopsy/">Prostate-MRI-US-Biopsy</a></li> <li>Links: <a href="https://doi.org/10.5281/zenodo.8290092">model weights, </a><a href="https://github.com/bamf-health/aimi-prostate-mr">github</a></li> </ul> <p> </p> <p><strong>Changelog</strong></p> <ul> <li>2.0.2 - Fix the brain-mr segmentations to be transformed correctly</li> <li>2.0.1 - added AIMI 2024 radiologist comments to qa-results.csv</li> <li>2.0.0 - added AIMI 2024 segmentations</li> <li>1.X - AIMI 2023 segmentations and reviewer scores</li> </ul>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
NMR and MS data of identified bromotyrosine alkaloids produced and released by Aplysina cavernicola
<p>This folder contains NMR and MS datasets of each identified bromotyrosine spiroisoxazoline pertaining to the publication<em> </em>entitled:</p> <p><strong>Diving into the molecular diversity of <em>Aplysina cavernicola’s </em>exo-metabolites: contribution of bromo-spiroisoxazoline alkaloids.</strong> <em>ACS Omega</em> 2022 <strong> <a href="https://pubs.acs.org/doi/10.1021/acsomega.2c05415"> </a></strong><a href="https://pubs.acs.org/doi/10.1021/acsomega.2c05415">https://doi.org/10.1021/acsomega.2c05415</a></p> <ul> <li>All NMR data were acquired in CD<sub>3</sub>OD at 600 MHz (Bruker Avance III, cryosonde TCI) using 2 mm NMR tubes</li> <li>All MS<sup>2</sup> data were acquired on a Bruker Impact II qTOF (ESI positive, collision energy 20-40eV)</li> </ul> <p>The compressed folder of the newly described Aplysine1 contains also raw data related to circular dichroism (CD) and infrared (IR) analyses, as well as quantum mechanical calculations of <sup>13</sup>C NMR shifts using GIAO NMR and DP4+ analyses.</p> <p>The Excel spreadsheet for DP4+ analyses were obtained from: Grimblat N et al. “Beyond DP4: An Improved Probability for the Stereochemical Assignment of Isomeric Compounds Using Quantum Chemical Calculations of NMR Shifts.” <em>The Journal of Organic Chemistry</em> 80, no. 24 (December 18, 2015): 12526–34. <a href="https://doi.org/10.1021/acs.joc.5b02396">https://doi.org/10.1021/acs.joc.5b02396</a>.</p> <p>All MS data are also made Freely available at the UCSD Center for Computational Mass Spectrometry database with the MassIVE identifier <a href="https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=4f6d3c00539a412a9c6d7fac0f7f2a81">MSV000089502</a> .</p> <p>NOTE: 3,5 dibromotyrosine was not identified neither in<em> Aplysina cavernicola </em>crude extract nor as exo-metabolites but was used for MS dereplication purposes.</p>
Nabro 3D velocity model produced by the FMTOMO code
<p>These files relate to "Seismic tomography of Nabro caldera, Eritrea: insights into the magmatic and hydrothermal systems of a recently erupted volcano" by Gauntlett et al., 2023. </p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability. Sources were originally located by Lapins et al. (<a href="https://doi.org/10.1029/2021JB021910">2021</a>), and the full catalogue is archived <a href="https://zenodo.org/record/7669717#.ZAXtfuymP0s">here</a> (Lapins, 2022). </p> <p>The FMTOMO package is freely available to download at<a href="http://rses.anu.edu.au/~nick/fmtomo.html"> http://rses.anu.edu.au/~nick/fmtomo.html</a>. </p> <p>This repository contains three 3D velocity models:</p> <ol> <li>vp_model.out</li> <li>vs_model.out</li> <li>vpvs_model.out</li> </ol> <p>These plain text files comprise the output of the FMTOMO tomography algorithm, which inverts for 3D P-wave velocity and S-wave velocity Vp and Vs) structure and Vp/Vs ratio for a grid centred around Nabro volcano. The velocity is given in km/s for Vp and Vs, and the values are dimensionless for Vp/Vs ratio.</p> <p>The grid is specified in the first four lines of the file:</p> <ul> <li>Line 1: for these data, this line will always hold the values `1' and `1'. </li> <li>Line 2: the number of grid nodes in radius (depth), latitude and longitude. </li> <li>Line 3: the radial (depth) node spacing in km, latitude spacing in radians, longitude spacing in radians.</li> <li>Line 4: the grid origin radius (km), latitude (radians) and longitude (radians).</li> </ul> <p>Each node of the grid has a P-wave, S-wave and Vp/Vs ratio associated with it, which is specified in lines 5 onward.</p> <ul> <li>Line 5 - 55229: Value of Vp, Vs or Vp/Vs (given by file name) at each node.</li> </ul> <p>The values loop over the grid, with longitude varying first, and radius last. The first node is at the grid origin. The second node is at the grid origin, plus the grid spacing in longitude. This continues for the `nlon` longitude nodes. The `nlon+1`the point is then at the grid origin, plus the latitude spacing; and so on.</p> <p>If there are `nr` radial nodes, `nlat` latitude nodes and `nlon` longitude nodes, then the following pseudocode shows how to read lines 5 forwards using the imaginary function `readline`, which reads a single real value from a plain text file and moves to the next line:</p> <p>```</p> <p># Comment: have already read the first four lines</p> <p>for ir in 1:nr:</p> <p>for ilat in 1:nlat:</p> <p>for ilon in 1:nlon:</p> <p>grid[ir,ilat,ilon] = readline(file_handle)</p> <p>```</p> <p>The repository also contains the event catalogue, with hypocenter locations after relocation by the FMTOMO code:</p> <p>4. event_catalogue.csv</p> <p>The columns are depth in km (negative values indicate depths below sea level, positive values indicate depths above sea level), latitude in degrees, longitude in degrees, depth error (km), latitude error (km), longitude error (km). </p>
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.