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53 results for “QA”
BioASQ-QA: A manually curated corpus for Biomedical Question Answering
<p>The BioASQ question answering (QA) benchmark dataset contains questions in English, along with golden standard (reference) answers and related material. The dataset has been designed to reflect real information needs of biomedical experts and is therefore more realistic and challenging than most existing datasets. Furthermore, unlike most previous QA benchmarks that contain only exact answers, the BioASQ-QA dataset also includes ideal answers (in effect summaries), which are particularly useful for research on multi-document summarization. The dataset combines structured and unstructured data. The material linked with each question comprise documents and snippets, which are useful for Information Retrieval and Passage Retrieval experiments, as well as concepts that are useful in concept-to-text Natural Language Generation. Researchers working on paraphrasing and textual entailment can also measure the degree to which their methods improve the performance of biomedical QA systems. Last but not least, the dataset is continuously extended, as the BioASQ challenge is running and new data are generated.</p>
Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs
<p>Event-QA dataset contains 1000 semantic queries and the corresponding verbalisations for EventKG - a recently proposed event-centric knowledge graph containing over 970 thousand events.</p>
Replication Package for "Software Quality Assurance Analytics: Enabling Software Engineers to Reflect on QA Practices" Paper (SCAM 2024)
<p>Welcome to our artifact!<br>In here we provide additional information for you to retrace our steps in the interview analysis.<br>It has the following contents:</p> <ul> <li><code>codebook.xlsx</code>: Our full codebook with our open codes, structured after the axial codes that emerged. <code>codebook-statistics.xlsx</code> lists for each code in which participant's interview it can be found.</li> <li><code>generate-figures</code>: The plain data and scripts used to generate the figures in the paper.</li> <li><code>survey.pdf</code>: An printout of our whole online questionnaire that guided the participants through the pretest-posttest study and the interview.</li> <li><code>survey-answers.xlsx</code>: The complete data for our participants answers in the online survey during the interviews.</li> <li><code>repoinsights-dashboard-software</code>: The code of our prototype repoinsights. As it is under active development, this is not yet documented for replicating the study setup or extending it. Still, we are providing the source code for transparency and will publish a version with comprehensive setup instructions later.</li> </ul>
Spoken QA
<p>Spoken questions automatically generated via Google Translate API for the following datasets (test splits only):</p> <ul> <li>Natural Questions <a href="https://ir-datasets.com/beir.html#beir/nq">https://ir-datasets.com/beir.html#beir/nq</a></li> <li>MS MARCO <a href="https://ir-datasets.com/beir.html#beir/msmarco/dev">https://ir-datasets.com/beir.html#beir/msmarco/dev</a></li> <li>Simple Questions (WikiData) <a href="https://github.com/askplatypus/wikidata-simplequestions">https://github.com/askplatypus/wikidata-simplequestions</a></li> </ul> <p>Script used for <a href="https://github.com/svakulenk0/spoken_qa/blob/main/notebooks/generate_speech.ipynb">speech generation</a></p> <p>The automated transcriptions for all spoken questions were made using the Facebook wav2vec2 large ASR model (wav2vec2-large-960h-lv60-self)</p> <p>More info on the project: <a href="https://github.com/svakulenk0/spoken_qa">github.com/svakulenk0/spoken_qa</a></p> <p> </p> <p> </p>
dataset related to article "On the evaluation of edgeless diode detectors for patient-specific QA in high-dose stereotactic radiosurgery"
<p>The database contains the comparison table between calculated doses and values actually measured by the edgeless detectors and an example of dose planar distribution as exported from the radiotherapy Treatment Planning System.</p> <p> </p>
ClimateIQA-QA-Pairs
<div>ClimateIQA comprises 8,760 high-resolution wind gust heatmap images and 254,040 question-answer pair samples. The dataset was created to address the crucial need for effective Extreme Weather Events Detection (EWED), an increasingly urgent task due to the rising frequency of such events driven by global warming. Traditional methods for EWED rely on numerical threshold setting and the analysis of weather anomaly heatmaps, visualizing data such as temperature, wind speed, and precipitation. However, these methods often involve manual work and can be time-consuming and error-prone. While advances in AI have led to the development of machine learning models like Convolutional Neural Networks (CNNs) for weather prediction and EWED, these models predominantly use numeric data and often yield low accuracy. Moreover, despite the proficiency of Large Language Models (LLMs) in generating textual weather reports, they struggle with interpreting visual data—crucial for EWED. General Vision-Language Models (VLMs) also face challenges in accurately interpreting meteorological heatmaps, commonly misidentifying colors, providing irrelevant responses, and giving incomplete answers. This dataset aims to fill these gaps by providing specialized meteorological data to fine-tune VLMs for more accurate and efficient EWED. </div> <div> </div> <div> </div>
GeoChanges QA
<p>This work introduces GeoChangesQA, a novel spatiotemporal QA dataset for historical geospatial knowledge. We first developed the Historical County Boundaries Ontology (HCB-O) to create this dataset. Then, we leveraged geospatial historical data spanning regions in the United States from 1630 to 2000 to populate the novel Historical County<br>Boundaries Knowledge Graph (HCB-KG). Subsequently, we developed a semi-automated procedure for generating questions, GeoSPARQL queries, and corresponding answers over HCB-KG by leveraging subgraph and query template extraction techniques. Through this method, we automatically generated 8, 900 question-query-answer triples.</p> <p>The code used to create this dataset is available here: <a href="https://github.com/AI-team-UoA/GeoChanges-Dataset">github repo</a></p> <p>The knowledge graph (rdf dumps) which the queries refer to is available here: DOI <a href="../doi/10.5281/zenodo.11508198">10.5281/zenodo.11508198</a> // <a title="Knowledge Graph" href="https://drive.google.com/drive/u/1/folders/1HFWT9dvkB8LRkhOxoLfFum8RRrIzkHQg">Knowledge Graph</a></p>
MTL-QA : A dataset and multi-task learning approach for knowledge graph and natural language question answering
<p>The dataset used for this project is created by enhancing the publicly available MetaQA (Movie Text Audio QA), which is primarily a KGQA dataset pertaining to movies, an extension of WikiMovies. This involves questions requiring 1, 2, and 3 hops which can be answered by using a MetaQA Knowledge Graph. The questions are available in text and audio format. The text has vanilla (original) and its paraphrased version, and is called ntm. </p> <p>In order to develop a dataset to support NLQA, a series of dataset augmentation steps has been performed.</p> <p>The dataset consists of natural language questions and a tagged topic entity as ground truth. This topic entity is used to retrieve textual information related to the question from Wikipedia. The introduction section of the entity's page is used as the context that is required for NLQA. Hence, this dataset has information related to both KGQA and NLQA. Certain preliminary checks and validations are done to only retain those data samples whose context can be used to answer a given question.</p>
Publication identifiers of the perovskite dataset and the QA code
<p>The dataset contains the identifiers for separate 194,322 publications used in the article 'Question Answering models for information extraction from perovskite materials science literature'. The publications are separated by the data provider. For articles scraped from Core API and arXiV API which did not contain DOI, the identifier is either arXiV API identifier or Core API identifier.</p> <p>The zipped folder "QA_for_MS-main.zip" contains the codes used in the article. They are also available in https://gitlab.com/mil-utu/QA_for_MS.</p>
Commensal bacteria maintain a Qa-1b-restricted unconventional CD8+ T population in gut epithelium
<p>Intestinal intraepithelial lymphocytes (IELs) are characterized by an unusual phenotype and developmental pathway, yet their specific ligands and functions remain largely unknown. Here by analysis of QFL T cells, a population of CD8+ T cells critical for monitoring the MHC I antigen processing pathway, we established that unconventional Qa-1b-restricted CD8+ T cells are abundant in intestinal epithelium. We found that QFL T cells showed a Qa-1b-dependent unconventional phenotype in the spleen and small intestine of naïve wild-type mice. The splenic QFL T cells showed innate-like functionality exemplified by rapid response to cytokines or antigens, while the gut population was refractory to stimuli. Microbiota was required for the maintenance, but not the initial gut homing of QFL T cells. Interestingly, monocolonization with <em>Pediococcus pentosaceus</em>, which expresses a peptide that cross-activated QFL T cells, was sufficient to maintain QFL T cells in the intestine. Thus, microbiota is critical for shaping the Qa-1b-restricted IEL landscape.</p>
Minecraft_QA-pairs_Instruction_Dataset
<h2>Overview</h2> <p>This repository contains a dataset designed for training large language models (LLMs) to handle a variety of questions related to Minecraft. Our dataset includes over 390,000 instruction entries generated using an LLM-assisted method. The dataset is designed to provide high-quality, diverse Q&A pairs, which are essential for effective model training.</p> <h2>Dataset Generation</h2> <h3>Data Collection and Cleaning</h3> <p>We utilized two primary sources of information:</p> <ol> <li><strong>Minecraft Fandom Wiki</strong> (<a href="https://minecraft.fandom.com/wiki/Minecraft_Wiki" rel="nofollow">https://minecraft.fandom.com/wiki/Minecraft_Wiki</a>)</li> <li><strong>Minecraft Wiki</strong> (<a href="https://minecraft.wiki/" rel="nofollow">https://minecraft.wiki/</a>)</li> </ol> <p>For both sources, we performed extensive data cleaning to remove irrelevant sections, such as game version information, achievements, advancements, and history. The remaining content was extracted and stored in text (TXT) and markdown (MD) files, ensuring that only useful text and table content were retained.</p> <h3>Q&A Pair Generation</h3> <p>Based on the cleaned data, we employed GPT-3.5-Turbo~\citep{chatgpt} with customized prompts to automatically generate diverse Q&A pairs. These pairs are categorized into four types:</p> <ol> <li><strong>Short Answer Q&A</strong>: Questions that can be directly answered with a word or phrase.</li> <li><strong>Long Answer Q&A</strong>: Questions that require one or more complete sentences for a comprehensive response.</li> <li><strong>Boolean Answer Q&A</strong>: Questions that can be answered with a simple True or False.</li> <li><strong>Normal Answer Q&A</strong>: General Q&A pairs with no specific form restrictions, ensuring the dataset's comprehensiveness.</li> </ol>
intron-innovation/AfriMed-QA: AfriMed-QA-v-1.2
<h2>What's Changed</h2> <ul> <li>Evalauting Pipeline for AfriMed-QA by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/1</li> <li>PR to support few shot evalution and instruction tuning by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/2</li> <li>Gpt4o by @tobiolatunji in https://github.com/intron-innovation/AfriMed-QA/pull/3</li> <li>PR to fix evaluation error. by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/4</li> <li>Ab eval by @tobiolatunji in https://github.com/intron-innovation/AfriMed-QA/pull/7</li> <li>edit prompt structure by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/8</li> <li>Gpt4o by @tobiolatunji in https://github.com/intron-innovation/AfriMed-QA/pull/10</li> <li>Gpt3.5turbo results by @SanniM3 in https://github.com/intron-innovation/AfriMed-QA/pull/9</li> <li>Gpt4o by @tobiolatunji in https://github.com/intron-innovation/AfriMed-QA/pull/11</li> <li>Gpt4o by @tobiolatunji in https://github.com/intron-innovation/AfriMed-QA/pull/14</li> <li>all results from my end by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/15</li> <li>results for gpt4turbo and claude sonnet by @SanniM3 in https://github.com/intron-innovation/AfriMed-QA/pull/16</li> <li>updated readme by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/18</li> <li>Inference results for meditron, pmc llama, mistral, and openbio by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/17</li> <li>claude opus results by @SanniM3 in https://github.com/intron-innovation/AfriMed-QA/pull/19</li> <li>Push results from claude_3_haiku run by @timfaniran in https://github.com/intron-innovation/AfriMed-QA/pull/20</li> <li>Results for MedLM by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/21</li> <li>Results for 7 models by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/22</li> <li>Support to Eval MedQA by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/23</li> <li>push new data version by @tobiolatunji in https://github.com/intron-innovation/AfriMed-QA/pull/24</li> <li>Update README.md by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/25</li> <li>PR for results from google models by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/26</li> <li>instruction tuning results by @SanniM3 in https://github.com/intron-innovation/AfriMed-QA/pull/27</li> <li>updated results for instruct and few shot - gpt4o and claude by @SanniM3 in https://github.com/intron-innovation/AfriMed-QA/pull/29</li> <li>Add files via upload by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/30</li> <li>MedQA Google Models by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/28</li> <li>More Google Results on MedQA by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/31</li> <li>Google Models: Fews shots + instruct Prompt by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/32</li> <li>Add files via upload by @tejuafonja in https://github.com/intron-innovation/AfriMed-QA/pull/33</li> <li>Medqa afrimed qa 3 shot by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/34</li> <li>Google models, Instruct Only by @owos in https://github.com/intron-innovation/AfriMed-QA/pull/35</li> <li>Update README.md by @princenimo in https://github.com/intron-innovation/AfriMed-QA/pull/36</li> </ul> <h2>New Contributors</h2> <ul> <li>@owos made their first contribution in https://github.com/intron-innovation/AfriMed-QA/pull/1</li> <li>@tobiolatunji made their first contribution in https://github.com/intron-innovation/AfriMed-QA/pull/3</li> <li>@SanniM3 made their first contribution in https://github.com/intron-innovation/AfriMed-QA/pull/9</li> <li>@princenimo made their first contribution in https://github.com/intron-innovation/AfriMed-QA/pull/18</li> <li>@timfaniran made their first contribution in https://github.com/intron-innovation/AfriMed-QA/pull/20</li> <li>@tejuafonja made their first contribution in https://github.com/intron-innovation/AfriMed-QA/pull/33</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/intron-innovation/AfriMed-QA/commits/version-1</p>
Commensal bacteria maintain a Qa-1b-restricted unconventional CD8+ T population in gut epithelium
Open the record for dataset details and reuse information.
Funerary Stela of King Qa'a
Funerary stela of the First Dynasty Pharaoh Qa'a in the Cairo Museum. Wall and nearby exhibit cases prevented capturing the back of the stone. Recovered from his tomb at Umm el-Qa'ab in Abydos, Egypt. Cairo Museum CG14631. Created from 76 photographs (Canon EOS Rebel T7i) taken on October 11, 2019 using Metashape 1.5.4. Object is scaled with dimensions in the .obj file in mm. Source: Objaverse 1.0 / Sketchfab
MRI Distortion QA Tool Phantom Test Dataset
<p>This data consists of CT and MRI Phantom images captured for the purpose of developing and testing an MRI Distortion QA tool and is released under the Creative Commons Attribution 3.0 Unported License.</p>
Supplementary material 1 from: Anibaba QA, Dyderski MK, Woźniak G, Jagodziński AM (2023) Native plant community characteristics explain alien species success in post-industrial vegetation. NeoBiota 85: 1-22. https://doi.org/10.3897/neobiota.85.97269
Frequency and cover of alien species occurring on post-coal mine spoil heaps
ECOSTRESS L3/L4 Ancillary data Quality Assurance (QA) flags L3 Global 70m V001
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO3ANCQA Version 1 is a Level 3 (L3) product that provides Quality Assessment (QA) fields for all ancillary data used in L3 and Level 4 (L4) products generated by Jet Propulsion Laboratory (JPL). No quality flags are generated for the L3 or L4 products. Instead, the quality flags of the source data products are resampled by nearest neighbor onto the geolocation of the ECOSTRESS scene. A quality flag array for each input dataset, when available, is collected into the combined QA product.The ECO3ANCQA Version 1 data product contains variables of quality flags for ECOSTRESS cloud mask, Landsat 8, land cover type, albedo, MODIS Terra aerosol, MODIS Terra Cloud 1 km, MODIS Terra Cloud 5 km, MODIS Terra atmospheric profile, vegetation indices, MODIS Terra gross primary productivity, and MODIS water mask.Known Issues* Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.
MODIS/Terra+Aqua BRDF/Albedo QA ValidobsBand5 Daily L3 Global 30ArcSec CMG V061
The MCD43D37 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 5 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D37 provides MODIS band 5 valid observation quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/).MCD43D37 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 5. Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).Improvements/Changes from Previous Versions* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).
MODIS/Terra+Aqua BRDF/Albedo QA ValidobsBand2 Daily L3 Global 30ArcSec CMG V006
The MCD43D34 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43D34 Version 6.1](https://doi.org/10.5067/MODIS/MCD43D34.061) data product.The MCD43D34 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 2 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D34 provides MODIS band 2 valid observation quality information for the MCD43D products. MCD43D34 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 2.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/). Known Issues* The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products.* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.* For complete information about MCD43D34 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).Improvements/Changes from Previous Version* Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period.* MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period.* Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day.* The MCD43 products use L2G-lite surface reflectance as input.* When there are insufficient high quality reflectances, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel.* CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid for MCD43D as opposed to aggregating from the 500 m albedo.
MODIS/Terra+Aqua BRDF/Albedo QA BRDFQuality Daily L3 Global 30ArcSec CMG V061
The MCD43D31 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA BRDF Quality dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. MCD43D31 provides BRDF/Albedo quality information for the MCD43D products. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43d-cmg-30-arc-second-products/).MCD43D31 consists of a BRDF quality layer representing the overall quality of each pixel along with an individual BRDF/Albedo quality layer for MODIS spectral bands 1 through 7. Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).Improvements/Changes from Previous Versions* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).
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.