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13,117 results for “Maps”
Empirical Research on Requirements Quality: A Systematic Mapping Study - Dataset
<p>We conducted a systematic mapping study on the empirical research into the quality of requirements titled "Empirical Research on Requirements Quality: A SystematicMapping Study" in the Requirements Engineering Journal. This is our replication package.</p> <p>Full Abstract:</p> <p>Research has repeatedly shown that high quality requirements are essential for the success of development projects. While the term “quality” is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6,905 articles from six academic databases, which we filtered down to our 105 relevant primary studies: explicitly defining, improving, or evaluating quality attributes while including an empirical research component. We found that research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as “template conformance” for consistency or “passive voice” for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as “requirements,” while little research targets quality attributes in specific types of requirements such as use cases or user stories. We present and discuss our detailed analysis along the various quality attributes. Our findings highlightthe need to conduct more grounded research aimed at “definitions,” to use more diverse research methods, and to address a more diverse set of requirements types.</p>
QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa [Main code]
<p>This is the main code supplement for my computational analysis for the manuscript: "QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species <em>Brassica oleracea </em>var<em>. villosa".</em> The main code is availabe in separate html-files. DOI will be added if available.</p>
Data related to research article: Towards mouse genetic-specific RNA-sequencing read mapping
<p>This dataset contains data related to the research article: "Towards mouse genetic-specific RNA-sequencing read mapping".</p>
Phonological feature mapping for FeatureTTS
<p>This is a mapping between:</p> <ul> <li>APRAbet and SAMPA</li> <li>Pinyin and SAMPA-SC</li> <li>SAMPA/SAMPA-SC and Phonological features (FUL-based)</li> </ul>
Map Shapefiles of Regional Geology of the Hypanis Valles System, Mars
<p>This dataset consists of shapefiles for the manuscript Regional Geology of the Hypanis Valles System, Mars by Adler et al. (under review). Inside the zipfile is a README explaining how to view the data and sort shape orders. The data represent a geomorphic map of the Hypanis Valles watershed and a geomorphic map of the Hypanis deposit region at its terminus. We mapped these two regions at different scales: 1:2,000,000 for the catchment map (-5-10°N, 300-315°E) and 1:500,000 for the Hypanis deposit map (10-13.0°N, 313-316.5°E). Our mapping provides new morphologic insights beyond previous efforts which used lower spatial resolution data. We defined units based on morphology, albedo, thermal inertia, elevation, and spectral parameters.</p>
Results from the revision of MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data
<p>afr47041.zip, lat36378.zip, and eur115620.zip contain All of Us Summary Statistics used in the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Summary statistics for three cohorts are included: Afr47k, Lat36k, and Eur116k. These cohorts have not been downsampled to have equal levels of missingness.</p> <p>pips.tsv contains fine-mapped variants with PIP > 0.01 via MultiSuSiE from the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Subcohorts with the _unmatched suffix have not been downsampled to have equal levels of phenotyped missingness across ancestries. </p> <p>MultiSuSiE-main.zip contains the MultiSuSiE software packages (corresponds to the Github repo on 10/16/2025).</p> <p>Please cite:</p> <p>Rossen, Jordan, et al. "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data." <em>medRxiv</em> (2024): 2024-05.</p> <p> </p>
Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping
<p>Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping</p>
Fracture maps and calving fronts for Thwaites Glacier western terminus 2015-2021
<p>These data comprise observations of severe crevassing and calving front position over the Thwaites Glacier Ice Tongue (TGIT) between 2015 and 2021 in geotiff form, along with bitmap versions of Sentinel-1 backscatter images from which the observations were derived. A version of UNet was used to create the data from the backscatter images.<br> These data were collected in 2021 for the study of structural change on the TGIT.</p> <p>File information: tgit_cfs.tar.gz is a gz-compressed directory of binary calving front segmentations of the Thwaites Glacier Ice Tongue in geotiff format.<br> tgit_fms.tar.gz is a gz-compressed directory of binary fracture segmentations of the Thwaites Glacier Ice Tongue in geotiff format.</p>
Safeconsume: Risk behaviour map
<p>The Risk-behaviour map is a document intended to aid access to and transfer of key data between research groups in the European project Safeconsume. The map covers only steps from retail to consumption for the case studies in Safeconsume where the consumer can reduce risk for foodborne infection (CCHs, Critical Consumer Handling). </p> <p>The map contains information about observed/reported behaviours that can affect risk for foodborne infection divided by country, consumer group, pathogen, food and step in the journey from retail to consumption.</p> <p>Details about data collection is given in: Skuland, S.E., Borda, D., Didier, P., Dumitras¸cu, L., Ferreira, V., Foden, M., Langsrud, S., Maître, I., Martens, L., Møretrø, T., Nguyen-The, C., Nicolau, A. I., Nunes, C., Rosenberg, T. G., Teigen, H. M., Teixeira, P., Truninger, M., 2020. European Food Safety: Mapping Critical Food Practices and Cultural Differences in France, Norway, Portugal, Romania and the UK, in: Skuland, S.E. (Ed.). SIFO report, Oslo. <a href="https://oda.oslomet.no/oda-xmlui/handle/20.500.12199/3112">ODA Open Digital Archive: European food safety: Mapping critical food practices and cultural differences in France, Norway, Portugal, Romania and the UK (oslomet.no)</a></p> <p>Questions about the RM-map can be raised to the SafeConsume project coordinator: <a href="mailto:Solveig.langsrud@nofima.no">Solveig.langsrud@nofima.no</a> </p> <p><strong>Variable list:</strong></p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Name </strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p>CCH/Critical steps </p> </td> <td> <p>Identification of the step and flow diagram the entry belongs to: The step in the flow diagram where the consumer through actions or choices can significantly reduce risk of foodborne infection </p> <p> </p> <p>The CCHs/critical steps belong to one of the following processes: Poultry and vegetables (PVF), Eggs (EGG), Shellfish (SHE), Ready-to-Eat (RTE). Each step is accompanied by the principle of risk reducing effect: </p> <p>Food choice: Buy or eat food with lower risk (e.g avoid buying food if not stored properly in shop, buying pasteurised products, choosing to eat food before use-by-date). Applies to all pathogens. </p> <p>Inhibit growth: Storing ready-to-eat food at cool temperature and consume within expiration date or adding preservatives. Applies to Listeria and Salmonella </p> <p>Wash/Remove: Wash vegetables and fruit. Applies to all pathogens </p> <p>Kill/Heat: Heat treatment to kill pathogens, freezing (Campylobacter) </p> <p>Personal hygiene: Avoid cross-contamination through hand washing or not touching food. Not preparing food when sick </p> <p>Hygiene: Avoid cross-contamination through washing surfaces and using clean utensils </p> </td> </tr> <tr> <td> <p>Cause or sources </p> </td> <td> <p>Description of causes and sources for the hazard to occur (presence, survival, transfer or growth of pathogen). See Appendix 3 for details </p> </td> </tr> <tr> <td> <p>Consumer Id </p> </td> <td> <p>Unique identifier of consumer. </p> </td> </tr> <tr> <td> <p>Pathogen </p> </td> <td> <p>The pathogen(s) that are relevant for the specific CCH/critical step </p> </td> </tr> <tr> <td> <p>Expert opinion: Effect on pathogen </p> </td> <td> <p>Effect of behaviour on the hazard estimated by a team of microbiologists. </p> </td> </tr> <tr> <td> <p>Effect on pathogen </p> </td> <td> <p>The effect on pathogen is an estimate of the change in the level of viable pathogens as a direct or indirect consequence of the behaviour, action or process. </p> </td> </tr> <tr> <td> <p>Consumer group, education, income, rural/urban and country </p> </td> <td> <p>When applicable, demographic data associated with the entry. </p> </td> </tr> </tbody> </table> <p><strong>Classification</strong></p> <table> <tbody> <tr> <td> <p>Name </p> </td> <td> <p>Attributes </p> </td> <td> <p>Classification, llist of codes/units </p> </td> </tr> <tr> <td> <p>CCH/Critical step </p> </td> <td> <p>Predefined, multiple choices </p> </td> <td> <table> <tbody> <tr> <td> <p>EGG 1 Food choice </p> </td> </tr> <tr> <td> <p>EGG 3.2 Hygiene </p> </td> </tr> <tr> <td> <p>EGG 4a Inhibit growth </p> </td> </tr> <tr> <td> <p>EGG 4b Inhibit growth </p> </td> </tr> <tr> <td> <p>EGG 5.1 Hygiene </p> </td> </tr> <tr> <td> <p>EGG 5.2 Personal hygiene </p> </td> </tr> <tr> <td> <p>EGG 6a Kill </p> </td> </tr> <tr> <td> <p>EGG 6b Kill </p> </td> </tr> <tr> <td> <p>EGG 6c Food choice </p> </td> </tr> <tr> <td> <p>EGG 6c Inhibit growth </p> </td> </tr> <tr> <td> <p>EGG 7.3 Inhibit growth </p> </td> </tr> <tr> <td> <p>EGG 8.3 Inhibit growth </p> </td> </tr> <tr> <td> <p>EGG 9.1 Inhibit growth </p> </td> </tr> <tr> <td> <p>EGG 11.3 Inhibit growth </p> </td> </tr> <tr> <td> <p>PVF 1.1 Food choice </p> </td> </tr> <tr> <td> <p>PVF 1.2 Food choice </p> </td> </tr> <tr> <td> <p>PVF 2.1 Inhibit growth </p> </td> </tr> <tr> <td> <p>PVF 3a Inhibit growth </p> <p>PVF 3a Kill </p> </td> </tr> <tr> <td> <p>PVF 3b Inhibit growth </p> </td> </tr> <tr> <td> <p>PVF 5.1 Personal hygiene </p> </td> </tr> <tr> <td> <p>PVF 5.2 Personal hygiene </p> </td> </tr> <tr> <td> <p>PVF 6.1 Kill </p> </td> </tr> <tr> <td> <p>PVF 7a Wash/Remove </p> </td> </tr> <tr> <td> <p>PVF 7b Personal hygiene </p> </td> </tr> <tr> <td> <p>PVF 7b Wash/Remove </p> </td> </tr> <tr> <td> <p>PVF 8b Hygiene </p> </td> </tr> <tr> <td> <p>PVF 8b Personal Hygiene </p> <p>PVF 9.1 Hygiene </p> </td> </tr> <tr> <td> <p>PVF 10.1 Hygiene </p> </td> </tr> <tr> <td> <p>PVF 11.1 Inhibit growth </p> <p>PVF 11.2 Inhibit growth </p> </td> </tr> <tr> <td> <p>RTE 1.1 Food choice </p> </td> </tr> <tr> <td> <p>RTE 3.1 Inhibit growth </p> </td> </tr> <tr> <td> <p>RTE 6.1 Inhibit growth </p> </td> </tr> <tr> <td> <p>RTE 4b Personal hygiene </p> </td> </tr> <tr> <td> <p>RTE 5.2 Hygiene </p> </td> </tr> <tr> <td> <p>RTE 5.2 Personal hygiene </p> </td> </tr> <tr> <td> <p>RTE 6.1 Inhibit growth </p> </td> </tr> <tr> <td> <p>RTE 7.1 Inhibit growth </p> </td> </tr> <tr> <td> <p>RTE 7.2 Personal hygiene </p> </td> </tr> <tr> <td> <p>SHE 1.1 Food choice </p> </td> </tr> <tr> <td> <p>SHE 7.1 Kill </p> <p>No risk </p> <p>Not designated to CCH </p> </td> </tr> </tbody> </table> <p> </p> </td> </tr> <tr> <td> <p>Causes/sources </p> </td> <td> <p>Free text </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Consumer ID </p> </td> <td> <p>Free text </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Pathogen </p> </td> <td> <p>Predefined, multiple choice </p> </td> <td> <ul> <li> <p>Salmonella: S. Enterica </p> </li> <li> <p>Campylobacter: C. jejuni </p> </li> <li> <p>Listeria: Listeria monocytogenes </p> </li> <li> <p>Norovirus </p> </li> <li> <p>Toxoplasma: Toxoplasma gondii </p> </li> </ul> </td> </tr> <tr> <td> <p>Expert opinion: Effect on pathogen </p> </td> <td> <p>Predefined </p> </td> <td> <ul> <li> <p>High reduction: This behaviour will have a high reduction on the level of pathogens on food/surfaces/hands </p> </li> <li> <p>Median reduction: This behaviour will reduce the level of pathogens on food/surfaces/hands </p> </li> <li> <p>No effect: This behaviour will most likely not have a significant effect on the level of viable pathogens (< 1 log10 reduction/increase or less than 10 cells/particles transfer) For food choice: Random choice is rated as no effect </p> </li> <li> <p>Median increase: This behaviour will lead to a higher number of pathogens </p> </li> <li> <p>High increase: This behaviour will significantly increase the number of pathogens on food/surfaces/hands </p> </li> </ul> </td> </tr> <tr> <td> <p>Effect on pathogen comment </p> </td> <td> <p>Free text </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Consumer group </p> </td> <td> <p>Predefined </p> <p> </p> </td> <td> <ul> <li> <p>Elderly; >70 years, men and women </p> </li> <li> <p>Pregnant women; immunocompromized </p> </li> <li> <p>Young family: Couples (married or cohabitant) where the women is pregnant or living with their own child(ren) (including stepchildren and adopted children) aged less than 12 months </p> </li> <li> <p>Young, single man: Men age 20-29, Living alone or with flatmates </p> </li> <li> <p>Other: Consumers not belonging to the defined groups </p> </li> </ul> </td> </tr> <tr> <td> <p>Educational level </p> </td> <td> <p>Predefined </p> </td> <td> <ul> <li> <p>Basic </p> </li> <li> <p>Secondary </p> </li> <li> <p>Tertiary </p> </li> <li> <p>Not given </p> </li> </ul> </td> </tr> <tr> <td> <p>Living area </p> </td> <td> <p>Predefined </p> </td> <td> <ul> <li> <p>Rural </p> </li> <li> <p>Urban </p> </li> </ul> </td> </tr> <tr> <td> <p>Income </p> </td> <td> <p>Predefined </p> </td> <td> <ul> <li> <p>Low </p> </li> <li> <p>Median </p> </li> <li> <p>High </p> </li> </ul> </td> </tr> <tr> <td> <p>Country </p> </td> <td> <p>Predefined </p> </td> <td> <p>Portugal, France, Romania, UK, Norway, Hungary, </p> </td> </tr> </tbody> </table>
Spectroscopic ellipsometry mapping of PAAO:DLC:Ag (AJ-8-03-31-DLCAg sample)
<p>Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO) covered with hydrogenated amorphous diamond-like carbon and silver (DLC:Ag) nanocomposite. The sample was made by 2 processes: (1) anodization of aluminum polycrystal in 0.3 mol/L oxalic acid at 40 V for 5 minutes and 4 seconds and then (2) depositing DLC:Ag employing reactive unbalanced magnetron sputtering in direct current mode using silver target (80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration).</p> <p>The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm<sup>2</sup>). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-8-03-31-DLCAg Ellipsometry Mapping Measurements.rar" file.</p> <p>Ellipsometer: rotating compensator GES5-E (Semilab).</p> <p>Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum.</p> <p>Detector: UV-Vis CCD with 0.8 nm resolution.</p> <p>Spectral range: approximately 230-960 nm.</p> <p>Light incidence angles: 55°, 60°, 65°, 70°, 75°.</p> <p>Light beam size: microspot (365 × 470 μm<sup>2</sup> at 75° angle of incidence).</p> <p>The same sample was also measured using the same spectroscopic ellipsometry method before being covered with DLC:Ag. The data can be found here: <a href="https://doi.org/10.5281/zenodo.7056065">https://doi.org/10.5281/zenodo.7056065</a></p>
Spectroscopic ellipsometry mapping of PAAO:DLC:Ag (AJ-7-03-31-DLCAg sample)
<p>Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO) covered with hydrogenated amorphous diamond-like carbon and silver (DLC:Ag) nanocomposite. The sample was made by 2 processes: (1) anodization of aluminum polycrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 35 seconds and then (2) depositing DLC:Ag employing reactive unbalanced magnetron sputtering in direct current mode using silver target (80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration).</p> <p>The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm<sup>2</sup>). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-7-03-31-DLCAg Ellipsometry Mapping Measurements.rar" file.</p> <p>Ellipsometer: rotating compensator GES5-E (Semilab).</p> <p>Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum.</p> <p>Detector: UV-Vis CCD with 0.8 nm resolution.</p> <p>Spectral range: approximately 230-960 nm.</p> <p>Light incidence angles: 55°, 60°, 65°, 70°, 75°.</p> <p>Light beam size: microspot (365 × 470 μm<sup>2</sup> at 75° angle of incidence).</p> <p>The same sample was also measured using the same spectroscopic ellipsometry method before being covered with DLC:Ag. The data can be found here: <a href="https://doi.org/10.5281/zenodo.7053393">https://doi.org/10.5281/zenodo.7053393</a></p>
Figures 42–47. Species distribution maps. 42 in Synopsis of the tribe Amarotypini in New Zealand (Coleoptera: Carabidae)
Figures 42–47. Species distribution maps. 42) Amarotypus glasgowensis new species. 43) Amarotypus murchisonorum new species. 44) Amarotypus simoninensis new species. 45) Amarotypus takaheensis new species. 46) Amaroxenus arnaudensis new species. 47) Amaroxenus glacialis new species.
Figures 36–41. Species distribution maps. 36 in Synopsis of the tribe Amarotypini in New Zealand (Coleoptera: Carabidae)
Figures 36–41. Species distribution maps. 36) Amarophilus lomondensis new species. 37) Amarophilus otagoensis new species. 38) Amarophilus rotundicollis new species. 39) Amarophilus wanakensis new species. 40) Amarotypus edwardsii. 41) Amarotypus fiordlandensis new species.
Figures 48–49. Species distribution maps. 48 in Synopsis of the tribe Amarotypini in New Zealand (Coleoptera: Carabidae)
Figures 48–49. Species distribution maps. 48) Amaroxenus huttensis new species. 49) Amaroxenus kahurangiensis new species.
Systematic Literature Mapping - Selected Articles Data Extraction
<p>- CSV with the raw data extracted from abstract + full-text review from the selected articles.</p> <p>- CSV with normalized data.</p> <p>- CSV with the normalized data after being a feature engineering step in which some columns have been deleted.</p> <p>- CSV with formal context to extract the concepts lattice.</p>
20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data
<p>This dataset provides 20 m annual paddy rice map for mainland Southeast Asia since 2019 .</p> <p>*** The data file is in “.tif" format</p> <p>*** Pixel size: 20 m</p> <p>*** Projection information: EPSG: 4326 (WGS84)</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Distribution map of Badorf ware (Northern Europe)
<p>Badorf ware, a pottery produced near Cologne (Germany) during the 8th and 9th centuries AD, was widely traded in the North Sea and Baltic area. The two files contain a distribution map (as csv-file) and additional bibliography for Northern Germany and Scandinavia.</p>
Excited-state photoemission band mapping data of the topological insulator Bi2Te2Se
<p>The dataset contains excited-state photoemission band mapping data of the topological insulator Bi2Te2Se.</p> <p><strong>Provenance</strong>; The measurements were carried out at the Fritz Haber Insititute of the Max Planck Society, Germany. The Bi2Te2Se sample was grown at Aarhus University, Denmark. The photoelectrons were detected using SPECS METIS 1000 3D detector with extreme UV pulses centered at 21.7 eV as probe and a pump light pulses centered at 800 nm, which populates part of the excited electronic states (i.e. first conduction band).</p> <p><strong>Content</strong>: The dataset comes from (1) binning of the single-electron event data and (2) preprocessing through symmetrization and MCLAHE algorithm (https://ieeexplore.ieee.org/document/8895993) for contrast adjustment</p> <p><strong>Usage</strong>: The dataset is the prerequisite for band structure reconstruction, which may be used for reproducing the results in (https://arxiv.org/abs/2005.10210). For reconstruction, see the source code and examples on GitHub (https://github.com/mpes-kit/fuller).</p>
Dataset of photoemission valence-band mapping and band reconstruction of 2H-WSe2
<p>Photoemission band mapping, band structure calculation, and reconstructed band dispersion of layered semiconductor 2<em>H</em>-WSe<sub>2</sub>, a type of 2D transition metal dichalcogenide.</p> <p><strong>Provenance</strong>: The experimental data were measured using the SPECS METIS 1000 3D detector with extreme UV pulses centered at 21.7 eV as the photoemission light source. The samples were commercially purchased and directly used in the experiment.</p> <p>The calculated data were converted and preprocessed from an existing record (http://dx.doi.org/10.17172/NOMAD/2020.03.28-1), which contains the band structure of 2<em>H</em>-WSe<sub>2</sub> calculated using density functional theory (DFT) at the level of LDA, PBE, PBEsol, and HSE06 exchange-correlation functionals using FHI-aims (version 171221_1).</p> <p><strong>Content</strong>: The data contain binned and preprocessed photoemission data; Preprocessed DFT calculations used for initializing the band reconstruction; Postprocessed band reconstruction data (including 14 identifiable valence bands).</p> <p><strong>Usage</strong>: The dataset may be used for reproducing the results in (https://arxiv.org/abs/2005.10210), computational benchmarks or separately for other materials science applications. For reconstruction, see the source code and examples on GitHub (https://github.com/mpes-kit/fuller).</p>
Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron
<p>We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT.</p> <p>The dataset consists of:</p> <ul> <li>inputs: dual-energy tomograms as binary files without a header (tensor <strong>shape for numpy: 2x128x128 @float32</strong>) <ul> <li>simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn</li> </ul> </li> <li>outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) <ul> <li>sampled with a delaunay algorithm and randomly filled with iron and copper fractions</li> </ul> </li> </ul> <p>The <strong>dataset is normalized to [0, 1]</strong>, so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.</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.