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Biomass removal at the initiation of a biodiversity experiment at the Jornada Basin LTER site in 1995
This dataset contains data on vegetation biomass removed from treatment plots during the establishment of a biodiversity experiment at the Jornada Basin LTER site in southern New Mexico, USA. In fall of 1995, various combinations of plant functional groups or species were experimentally removed from 25 x 25 meter plots with the objective to distinguish the differential effects of plant community biomass, functional groups, and biodiversity within functional groups on ecosystem and plant community function. Eight different treatments were established with selective removal of species or functional groups: control (C, no removals); four functional group removal treatments (PG, perennial grass removed; S, shrubs removed; SSh, subshrubs removed; Succ, succulents removed), and three species richness manipulation treatments. Richness manipulations included a simplified treatment (Simp), where only the single most abundant species of each growth form is preserved and all other species in the growth form are removed, a reduced‐Larrea treatment (rL), where the Larrea is assumed to be the dominant and is removed while minority components remain, and a reduced-Prosopsis treatment (rP), where Prosopis rather than Larrea is removed as the shrub dominant. The amount of plant material removed during the establishment of these treatments was recorded for later use as covariate or measure of disturbance. Removed fresh material was weighed in the field by species, then converted to dry mass using a subset of removed plot vegetation that was oven-dried and weighed in the lab. Variables in this file summarize the dry mass of plants removed by growth form (functional group, i.e. shrub, subshrub, perennial grass, succulent), and by total live dry mass, for each plot in the biodiversity experiment. Also provided are masses of dead material collected from plots (same groups as live material removed for each treatment) and total dry mass, live plus dead. Species-level data are available upo
Social Processes Initiative in Neurobiology of the Schizophrenia(s) Traveling Human Phantoms
Open the record for dataset details and reuse information.
Initial Spread Index - ERA-Interim
<p>The Initial Spread Index (ISI) is a numeric rating of the expected rate of fire spread. It combines the effects of wind and the FFMC on rate of spread without the influence of variable quantities of fuel.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the ATB HYPERNETS site in Marquardt, Germany [52°27'59.40"N, 12°57'35.16"E] (ATGE). It is a subset of the complete data record, consisting of the measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 dgrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 11 Oct 2022 at the top of a 5m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 52.466778°N, 12.959778°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angle.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full ATGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)
We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.
Phenology of flowers and leaves following experimental warming in the initiation and maturation years for 7 understory boreal plants at the Bonanza Creek Long Term Ecological Research (BNZ LTER) site in Interior Alaska: 2017-2019
This dataset contains the results of experimental warming of flower and leaf buds for 7 understory boreal plants: Rhododendron groenlandicum, Rosa acicularis, Rubus chamaemorus, Shepherdia canadensis, Viburnum edule, Vaccinium uliginosum, and Vaccinium vitis-idaea. Plants in two cohorts were subjected to one of four treatments: warming in the initiation year (the year prior to flowering or leaf-out) only, warming in the maturation year (the year of flowering or leaf-out) only, warming in both years, or no warming (controls). The timing of flowering (both cohorts) and leaf-out (usually one cohort) was monitored. We also tracked developmental stages of the flower bud primordia using repeated desctructive sampling followed scanning electron microscopy throughout the initiation years. Environmental data associated with the plots, including air temperature throughout the summer, soil temperature and depth of ground thaw in late May, and canopy cover, are also reported.
MCR LTER: Coral Reef: Population Dynamics: Symbiodinium population initial sampling conditions in corals of Moorea
These data consist of sample collection locations and conditions only, not the results of sequencing. These data were an initial collection of an expected time series which was not repeated. Clade data derived from the sample collection described here are archived on GeoSymbio. Coral are symbiotic with photosynthetic dinoflagellates from the genus Symbiodinium. There are eight divergent clades (A-H) within the genus which each contain many genetic varieties. Different types of Symbiodinium have been shown to affect the growth rate and thermal tolerance of the coral. The diversity and population structure of Symbiodinium on a reef and how it changes over time is therefore critical to the stability and resilience of coral reefs. Our research conducted through the MCR LTER project aims at defining the population structure of Symbiodinium present in the corals of Moorea and documenting changes over time. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Alaska 2020 update for USGS G19AP00019: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2020 uses earthquake travel-time and ambient noise group velocity data to update Eberhart-Phillips et al. (2006: AKEP2006)</p> <p> </p> <p>The model is provided in a table: vlAKEP2020xyzltlnSFDRE.tbl.txt</p> <p>and in the simul output from velocity inversion: vlAKEP2020.out.txt</p> <p>Map plots of Vp and Vp/Vs are also provided, with lines denoting limits of adequate data.</p> <p> </p> <p>Velocity Inversion Procedure Notes for AKEP2020 model</p> <p> </p> <p>The 2006 AK model and the 2020 updated model both use Transverse Mercator coordinate transformation with central meridian= -150 and counterclockwise rotation of 162.1. Earth-flattening transformation is used for velocity during ray-tracing. The depths are relative to sea-level and station elevations are used. For the group-velocity data, the surface is taken as the 30-km median filtered topography.</p> <p>Velocity with the 3D gridded model is defined by linearly interpolating between nodes.</p> <p>A gradational inversion approach was used with earthquake and shot travel-time data, and group velocity observations for periods 6-15 s. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p> </p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G19AP00019. Note that an earlier model, AKEP2018, from the first year of this funded project was reported in Eberhart-Phillips et al. (2019).<br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey</p>
SPARC Data Initiative monthly zonal mean composition measurements from stratospheric limb sounders (1978-2018)
<p>The SPARC Data Initiative dataset is the most comprehensive compilation of vertically resolved stratospheric composition measurements to date and consists of four decades of monthly zonal mean climatologies (1978-2018) from a range of satellite limb sounders including LIMS, SAGE I/II/III, HALOE, UARS-MLS, POAMII/III, OSIRIS, SMR, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, SMILES, OMPS-LP and SAGE III-ISS. The dataset includes most major long-lived trace gases (O<sub>3</sub>, H<sub>2</sub>O, N<sub>2</sub>O, CH<sub>4</sub>, CCl<sub>3</sub>F, and CCl<sub>2</sub>F<sub>2</sub>), transport tracers (HF, SF<sub>6</sub>, HCl, CO, HNO<sub>3</sub>, NOy), and shorter-lived trace gases important to stratospheric chemistry including nitrogens (NO, NO<sub>2</sub>, NOx, N<sub>2</sub>O<sub>5</sub>,and HNO<sub>4</sub>), halogens (BrO, ClO, ClONO<sub>2</sub> and HOCl), and other minor species (OH, HO<sub>2</sub>, CH<sub>2</sub>O, CH<sub>3</sub>CN). The observations considered have been compiled in units of volume mixing ratio (VMR) and on a common latitude-pressure grid, covering the region from the upper troposphere to the lower mesosphere (300-0.1 hPa) with a latitudinal resolution of 5 degrees.</p> <p> </p>
Dataset of the manuscript "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment"
<p>The present database belongs to the manuscript titled "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment". The study has been peformed in English, but the research is conducted in Spanish.</p>
Supplemental Information - Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling
<p>This folder contains</p> <p>1) maps of plasmids</p> <p>2) files of phylogenetic analysis </p> <p>3) Replication information</p> <p>4) Image cropping information</p> <p>5) Gene IDs and protein sequences</p> <p>that are part of the manuscript "Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling"</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>
The oxygen initial dip in the brain of anesthetized and awake mice
<p>Datasets containing the data used to generate the figures and results from <a href="https://doi.org/10.1073/pnas.2200205119"><em>The oxygen initial dip in the brain of anesthetized and awake mice</em>, Aydin et al. (PNAS, 2022)</a>, along with the corresponding MATLAB code on <a href="https://github.com/alike-aydin/InitialDip_AydinEtAl">GitHub</a>. Download the file, and unzip it in the same folder as the scripts from the GitHub repository.</p>
Lake mask and distance to land dataset of 2024 lakes for the European Space Agency Climate Change Initiative Lakes v2
<p>This dataset contains the distance to land and the lake identifiers as a global netcdf file for all the water pixels at 1km (1/120 deg) lat/lon resolution of 2024 lakes distributed globally. It contains also the list of lakes as a csv file with information such as the lake center as defined in [1], and the coordinate of a box to easily locate the like in the global netcd file. The mask excludes islands on lakes and it has been derived from the GloboLakes high resolution limnology dataset [2]. The dateset have been further harmonized with the lake maximum extent lake polygons by PML [3]. The lake list with the plot of the mask and the polygons is available as a html file accessible also from the lake website at the University of Reading: http://www.laketemp.net/home_CCI/LMPolygons.php</p> <p>This dataset accompanies the <strong>ESA CCI Lakes v2 dataset</strong> [4].</p> <p> </p> <p>[1] Carrea, L.; Embury, O.; Merchant, C.J. (2015): High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates - Geoscience Data Journal, 2 (2). pp. 83-97. ISSN 2049-6060 doi: https://doi.org/10.1002/gdj3.32</p> <p>[2] Carrea, L.; Embury, O.; Merchant, C.J. (2015): GloboLakes: high-resolution global limnology dataset v1. Centre for Environmental Data Analysis. doi:10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb. <a href="http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb">http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb</a></p> <p>[3] Simis, S.; Mata, A.; Selmes, N.; Carrea, L. (2021) Lake polygons dataset accompanying Calimnos v1.4.0 and ESA CCI Lakes Climate Research Data Package v2.0. zenodo https://doi.org/10.5281/zenodo.4899250</p> <p>[4] Carrea, L.; Crétaux, J.-F.; Liu, X.; Wu, Y.; Bergé-Nguyen, M.; Calmettes, B.; Duguay, C.; Jiang, D.; Merchant, C.J.; Mueller, D.; Selmes, N.; Simis, S.; Spyrakos, E.; Stelzer, K.; Warren, M.; Yesou, H.; Zhang, D. (2022): ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.1. NERC EDS Centre for Environmental Data Analysis <a href="https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472">https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472</a></p>
5D-NP-FABTECH_ALD - Open Dataset for: "Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films"
<p>This is the open dataset for the paper: "Demelius, L. <em>et al.</em> Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films. <em>Applied Surface Science</em> <strong>604</strong>, (2022)."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Ultrafast imaging recordings from the axon initial segment of neocortical layer-5 pyramidal neurons.
<p>This dataset contains imaging and whole-cell electrophysiological recordings from neocortical layer-5 pyramidal neuron from brain slices of the mouse.</p> <p>Electrophysiological recordings (at 20 kHz) are from the soma. Imaging data (10 kHz) are from lines along the axon initial segment (distal>proximal) with 500 nm pixel resolution. These correspond to:</p> <ul> <li>Sodium imaging (Figures 1 and S6).</li> <li>Voltage imaging (Figures 2,4,5,S4,S7)</li> <li>Calcium imaging (Figures 3,S3,S8).</li> </ul> <p>This dataset is used in the paper available online:</p> <p>Filipis L, Blömer LA, Montnach J, De Waard M, Canepari M. Nav1.2 and BK channels interaction shapes the action potential in the axon initial segment. bioRxiv, 2022. doi: 10.1101/2022.04.12.488116.</p>
Alaska 2018 update for USGSG18AP00017: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2018 uses earthquake travel-time and ambient noise group velocity data to update the Alaska 3-D model of Eberhart-Phillips et al. (2006: AK2006), for the USGS project on developing Alaska Community Seismic Velocity Models . This 2018 model will be expanded with additional data in 2019 in the second year of the funded project.</p> <p>Velocity within the 3D gridded model is defined by linearly interpolating between nodes. The inversion solved for Vp and Vp/Vs. The model is provided in a table: vlAKEP2018xyzltlnSFDRE.tbl.txt, with velocity at inversion nodes in cartesian and latitude-longitude coordinates. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G18AP00017. <br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.</p>
S47 | ECHAPLASTICS | A list from the Plastic Additives Initiative Mapping Exercise by ECHA
<p>This is the collection associated with list S47 ECHAPLASTICS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S47</p> <p>ECHAPLASTICS</p> <p><strong>A list from the Plastic Additives Initiative Mapping Exercise by ECHA</strong></p> <p>Merged ECHA Plastic Additives with Structures <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/ECHA_PlasticAdditivesInitiative_06032019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/ECHA_PlasticAdditivesInitiative_06032019.csv">CSV</a> (06/03/2019)</p> <p>ECHA Plastic Additives <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/ECHA_Plastics_InChIKeys.txt">InChIKeys</a> (06/03/2019)</p> <p>List with several categories released on <a href="https://echa.europa.eu/mapping-exercise-plastic-additives-initiative">https://echa.europa.eu/mapping-exercise-plastic-additives-initiative</a> and mapped to structures by CAS and Name by E. Schymanski. </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>
Offering ART refill through community health workers versus clinic-based follow-up after home-based same-day ART initiation in rural Lesotho: The VIBRA cluster-randomised clinical trial
<p>These are pseudo-anonymised data from the VIBRA randomized trial: "Offering ART refill through community health workers versus clinic-based follow-up after home-based same-day ART initiation in rural Lesotho: The VIBRA cluster-randomised clinical trial". The data dictionary explains the data available in the dataset. Between August 2018 and May 2019, 257 eligible individuals from 117 consenting villages were enrolled from two districts of Lesotho, and followed up for a maximum of 15 months. The protocol was published, doi: 10.1186/s13063-019-3510-5</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.