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Updated studies.json
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‎dump/studies.json‎

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"name": "1000 Genomes",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "http://www.internationalgenome.org/formats",
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"type": "s3",
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"arn": "arn:aws:s3:::1000genomes",
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"region": "us-east-1"
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}],
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"description": "http://www.internationalgenome.org/formats",
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"type": "s3",
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"arn": "arn:aws:s3:::1000genomes",
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"region": "us-east-1"
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}],
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"moreInformation": "License: Data from the 1000 Genomes Project is now available without embargo, following the final publication from the project. Use of the data should be cited in the usual way, with current details available at http://www.internationalgenome.org/faq/how-do-i-cite-1000-genomes-project",
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"__v": 0,
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"isDeleted": false,
@@ -59,11 +59,11 @@
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"name": "IRS 990 Filings (Spreadsheets)",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "Excerpts of electronic Form 990 and 990-EZ filings, converted to spreadsheet form",
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"type": "s3",
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"arn": "arn:aws:s3:::irs-990-spreadsheets",
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"region": "us-east-1"
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}],
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"description": "Excerpts of electronic Form 990 and 990-EZ filings, converted to spreadsheet form",
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"type": "s3",
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"arn": "arn:aws:s3:::irs-990-spreadsheets",
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"region": "us-east-1"
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}],
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"moreInformation": "License:Attribution 4.0 International [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)",
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"__v": 0,
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"isDeleted": false,
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"_id": {
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"$oid": "5ff442596ae1c429994084d6"
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},
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"description": "ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. This representation of ChEMBL is stored in Parquet format and most easily utilized through Amazon Athena. Follow the documentation for install instructions (< 2 minute install). New ChEMBL releases occur sporadically; the most up to date information on ChEMBL releases can be found [here](https://chembl.gitbook.io/chembl-interface-documentation/downloads).",
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"description": "ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. This representation of ChEMBL is stored in Parquet format andmost easily utilized through Amazon Athena. Follow the documentation for install instructions (< 2 minute install). New ChEMBL releases occur sporadically; the most up to date information on ChEMBL releases can be found [here](https://chembl.gitbook.io/chembl-interface-documentation/downloads).",
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"tags": [
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"chemistry",
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"genomic",
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"_id": {
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"$oid": "5ff446206ae1c429994084d9"
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},
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"description": "An open multi-sensor dataset for autonomous driving research. This dataset comprises semantically segmented images, semantic point clouds, and 3D bounding boxes. In addition, it contains unlabelled 360 degree camera images, lidar, and bus data for three sequences. We hope this dataset will further facilitate active research and development in AI, computer vision, and robotics for autonomous driving.",
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"description": "An open multi-sensor dataset for autonomous driving research. This dataset comprises semantically segmented images, semantic pointclouds, and 3D bounding boxes. In addition, it contains unlabelled 360 degree camera images, lidar, and bus data for three sequences. We hope this dataset will further facilitate active research and development in AI, computer vision, and robotics for autonomous driving.",
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"tags": [
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"autonomous vehicles",
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"deep learning",
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"name": "A2D2: Audi Autonomous Driving Dataset",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "http://a2d2.audi",
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"type": "s3",
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"arn": "arn:aws:s3:::aev-autonomous-driving-dataset",
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"region": "eu-central-1"
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}],
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"description": "http://a2d2.audi",
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"type": "s3",
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"arn": "arn:aws:s3:::aev-autonomous-driving-dataset",
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"region": "eu-central-1"
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}],
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"moreInformation": "License:https://creativecommons.org/licenses/by-nd/4.0/",
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"__v": 0,
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"isDeleted": false,
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"name": "Allen Mouse Brain Atlas",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "Project data files in a public bucket",
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"type": "s3",
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"arn": "arn:aws:s3:::allen-mouse-brain-atlas",
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"region": "us-west-2"
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}]
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"description": "Project data files in a public bucket",
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"type": "s3",
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"arn": "arn:aws:s3:::allen-mouse-brain-atlas",
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"region": "us-west-2"
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}],
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"moreInformation": "License:http://www.alleninstitute.org/legal/terms-use/",
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"__v": 0,
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"isDeleted": false,
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"name": "3000 Rice Genomes Project",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "http://s3.amazonaws.com/3kricegenome/README-snp_pipeline.txt",
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"type": "s3",
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"arn": "arn:aws:s3:::3kricegenome",
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"region": "us-east-1"
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}],
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"description": "http://s3.amazonaws.com/3kricegenome/README-snp_pipeline.txt",
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"type": "s3",
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"arn": "arn:aws:s3:::3kricegenome",
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"region": "us-east-1"
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}],
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"moreInformation": "License: This data is available for anyone to use under the terms of the Toronto Statement, which is available [here](http://www.nature.com/nature/journal/v461/n7261/box/461168a_BX1.html)",
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"__v": 0,
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"isDeleted": false,
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"_id": {
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"$oid": "5ff4453a6ae1c429994084d8"
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},
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"description": "By constructing a virtual constellation of complementary types of satellites, we integrated the merit of high spatial resolution Landsat data with that of high temporal frequency MODIS data, to produce a high spatial-resolution and temporally consistent, better data set overcoming a number of shortcomings that conventional Earth observation (EO) data have, including cloud effects, data damage or loss. To do so, we developed a spatial-temporal remote sensing data reconstruction and fusion framework with an automated, serverless production chain on the AWS. Based on it, we produced the seamless data cube (SDC) at a 30 m spatial resolution and daily interval in an analysis-ready-data (ARD) format. The constructed fine-grained SDC will significantly reduce the preprocessing burden of users, broaden the use of remotely sensed data to a wider range of communities, and give us the capacity of near-real-time EO. Such kind of data is a long-term dream in the remote sensing and application community, that has never been realized before. The data set will promote new knowledge discovery on patterns, and benefit the land science community for biophysical, and socio-economic information extraction from the ARD data, making it easy and convenient to assess various policy goals such as conservation of protected areas, supporting international policy making on climate change mitigation, and raising awareness on environmental issues. The knowledge gained from this uniquely comprehensive data set will form a new foundation for achieving the United Nations (UN) Sustainable Develop Goals (SDGs).",
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"description": "By constructing a virtual constellation of complementary types of satellites, we integrated the merit of high spatial resolution Landsat data with that of high temporal frequency MODIS data, to produce a high spatial-resolution and temporally consistent, better data set overcoming a number of shortcomings that conventional Earth observation (EO) data have, including cloud effects, data damage or loss. To do so, we developed a spatial-temporal remote sensing data reconstruction and fusion framework with an automated, serverless production chain on the AWS. Based on it, weproduced the seamless data cube (SDC) at a 30 m spatial resolution and daily interval in an analysis-ready-data (ARD) format. The constructed fine-grained SDC will significantly reduce the preprocessing burden of users, broaden the use of remotely sensed data to a wider range of communities, and give us the capacity of near-real-time EO. Such kind of data is a long-term dream in the remote sensing and application community, that has never been realized before. The data set will promote new knowledge discovery on patterns, and benefit the land science community for biophysical, and socio-economic information extraction from the ARD data, making it easy and convenient to assess various policy goals such as conservation of protected areas, supporting international policy making on climate change mitigation, and raising awareness on environmental issues. The knowledge gained from thisuniquely comprehensive data set will form a new foundation for achieving the United Nations (UN) Sustainable Develop Goals (SDGs).",
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"tags": [
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"agriculture",
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"cities",
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"name": "21st Century Daily Global Seamless Remote Sensing Data Cubes (SDCs)",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "Daily Seamless Remote Sensing Data Cube (SDC).",
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"type": "s3",
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"arn": "arn:aws:s3:::sdc-daily-thu",
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"region": "us-west-2"
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}]
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"description": "Daily Seamless Remote Sensing Data Cube (SDC).",
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"type": "s3",
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"arn": "arn:aws:s3:::sdc-daily-thu",
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"region": "us-west-2"
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}],
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"moreInformation": "License:Open to non-commercial uses.",
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"__v": 0,
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"isDeleted": true,
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"_id": {
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"$oid": "5ff447456ae1c429994084da"
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},
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"description": "This dataset contains soil infrared spectral data and paired soil property\nreference measurements for georeferenced soil samples that were collected\nthrough the Africa Soil Information Service (AfSIS) project, which lasted\nfrom 2009 through 2018. In this release, we include data collected during\nPhase I (2009-2013.) Georeferenced samples were collected from 19 countries\nin Sub-Saharan African using a statistically sound sampling scheme,\nand their soil properties were analyzed using *both* conventional soil\ntesting methods and spectral methods (infrared diffuse reflectance\nspectroscopy). The two types of data can be paired to form a training\ndataset for machine learning, such that certain soil properties can be\nwell-predicted through less expensive spectral techniques.\n",
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"description": "This dataset contains soil infrared spectral data and paired soil property\nreference measurements for georeferenced soil samples that were collected\nthrough the Africa Soil Information Service (AfSIS) project, which lasted\nfrom 2009 through 2018. In this release, we include data collected during\nPhase I (2009-2013.) Georeferenced samples were collected from 19 countries\nin Sub-Saharan African using a statistically soundsampling scheme,\nand their soil properties were analyzed using *both* conventional soil\ntesting methods and spectral methods (infrared diffuse reflectance\nspectroscopy). The two types of data can be paired to form a training\ndataset for machine learning, such that certain soil properties can be\nwell-predicted through less expensive spectral techniques.\n",
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"tags": [
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"agriculture",
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"aws-pds",
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"name": "Africa Soil Information Service (AfSIS) Soil Chemistry",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "Paired wet and dry chemistry measurements for georeferenced soils\ncollected by the Africa Soil Information Service (AfSIS), stored\nas CSV and OPUS files.\n",
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"type": "s3",
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"arn": "arn:aws:s3:::afsis",
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"region": "us-east-1"
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}],
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"moreInformation": "License:ODC Open Database License (\"[ODbL](https://opendatacommons.org/licenses/odbl/summary/index.html)\") version 1.0, with attribution to AfSIS",
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"description": "Paired wet and dry chemistry measurements for georeferenced soils\ncollected by the Africa Soil Information Service (AfSIS), stored\nas CSV and OPUS files.\n",
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"type": "s3",
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"arn": "arn:aws:s3:::afsis",
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"region": "us-east-1"
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}],
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"moreInformation": "License:ODC Open Database License (\"[ODbL](https://opendatacommons.org/licenses/odbl/summary/index.html)\") version 1.0, withattribution to AfSIS",
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"__v": 0,
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"isDeleted": false,
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"repositoryName": "Registry of Open Data on AWS",
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"_id": {
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"$oid": "5ff447c86ae1c429994084db"
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},
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"description": "Dataset associated with the paper \"Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis\". Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. In its current stage, we have captured 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 2017 and 2019 across multiple growing seasons in numerous farming locations in the US. Each field image contains four color channels: Near-infrared (NIR), Red, Green and Blue. We first randomly split the 3,432 farmland images with a 6/2/2 train/val/test ratio. We then assign each sampled image to the split of the farmland image they are cropped from. This guarantees that no cropped images from the same farmland will appear in multiple splits in the final dataset. The generated Agriculture-Vision dataset thus contains 56,944/18,334/19,708 train/val/test images.",
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"description": "Dataset associated with the paper \"Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis\". Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. In its current stage, we have captured 94,986 512x512images sampled from 3,432 farmlands with nine typesof annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 2017 and 2019 across multiple growing seasons in numerous farming locations in the US. Each field image contains four color channels: Near-infrared (NIR), Red, Green and Blue. We first randomly split the 3,432 farmland images with a 6/2/2 train/val/test ratio. We then assign each sampled image to the split of the farmland image they are cropped from. This guarantees that no cropped images from the same farmland will appear in multiple splits in the final dataset. The generatedAgriculture-Vision dataset thus contains 56,944/18,334/19,708 train/val/test images.",
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"tags": [
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"aerial imagery",
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"agriculture",
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"name": "AgricultureVision",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "Terms of use and paper provided. Dataset provided as a series of tar.gz files with data foreach year and an associated json file dscribing the train/validation/test split.",
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"type": "s3",
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"arn": "arn:aws:s3:::agriculture-vision",
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"region": "us-east-1",
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"RequesterPays": true
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}],
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"description": "Terms of use and paper provided. Dataset provided as a series of tar.gz files with data foreach year and an associated json file dscribing the train/validation/test split.",
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"type": "s3",
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"arn": "arn:aws:s3:::agriculture-vision",
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"region": "us-east-1",
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"RequesterPays": true
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}],
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"moreInformation": "License:Provided in the bucket.",
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"__v": 0,
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"isDeleted": false,
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"_id": {
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"$oid": "5ff448606ae1c429994084dc"
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},
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"description": "The Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniques for measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, collected under analogous conditions to the two-photon imaging experiments. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.\n",
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"description": "The Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniquesfor measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, collected under analogous conditions to the two-photon imaging experiments. We hope that experimentalists and modelers will usethese comprehensive, open datasets as a testbed for theories of visual information processing.\n",
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"tags": [
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"aws-pds",
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"neurobiology",
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"name": "Allen Brain Observatory - Visual Coding AWS Public Data Set",
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"studyType": "Public",
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"resourceDetails": [{
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"description": "Project data files in a public bucket",
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"type": "s3",
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"arn": "arn:aws:s3:::allen-brain-observatory",
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"region": "us-west-2"
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}],
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"description": "Project data files in a public bucket",
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"type": "s3",
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"arn": "arn:aws:s3:::allen-brain-observatory",
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"region": "us-west-2"
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}],
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"moreInformation": "License:http://www.alleninstitute.org/legal/terms-use/",
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"__v": 0,
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"isDeleted": false,

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