NASA Open Data Public Data List. This machine readable file contains meta-records for NASA open data sets.

The INTEGRAL Public Data Results Catalog is based on publicly available data from the two main instruments (IBIS and SPI) on board INTEGRAL (see Winkler et al. 2003, A&A, 411, L1 for a description of the INTEGRAL spacecraft and instrument packages). INTEGRAL began collecting data in October 2002. This catalog will be regularly updated as data become public (~14 months after they are obtained). This catalog is a collaborative effort between the INTEGRAL Science Data Center (ISDC) in Switzerland and the NASA Goddard Space Flight Center (GSFC) INTEGRAL Guest Observer Facility (GOF). The results presented here are a result of a semi-automated analysis and they should be considered as approximate: they are intended to serve as a guideline to those interested in pursuing more detailed follow-up analyses. The data from the imager ISGRI (Lebrun et al. 2003, A&A, 411, L141) have been analyzed at the INTEGRAL Science Data Centre (ISDC), while the SPI (Vedrenne et al. 2003, A&A, 411, L63) data analysis was performed at GSFC as a service of the INTEGRAL GOF. Note: For cases where two or more proposals have been amalgamated (entries with pi_lname = 'Amalgamated') for a given observation, the same observation is listed for each of the amalgamated proposal numbers. This database table was first created in September 2004. It is based on the online web page maintained by the INTEGRAL GOF at the URL http://heasarc.gsfc.nasa.gov/docs/integral/obslist.html and was updated on a weekly basis whenever that web page was updated. Automatic updates were discontinued in June 2019. Duplicate entries were removed in June 2019, also. This is a service provided by NASA HEASARC .

This database table is a catalog of all the RXTE slew observations and is based on information culled from the RXTE Data Archive's latest top-level FMI (FITS Master Index) file that is created when data products are made publicly available each week. ObsIDs listed in this table are available for download from https://heasarc.gsfc.nasa.gov/FTP/xte/data/archive/. See the parameter 'data_loc' for the relative location of specific ObsIDs. The XTESLEW database table is updated automatically, usually on a weekly basis whenever the RXTE GOF updates the top-level FMI for the public data archive and notifies the HEASARC. This is a service provided by NASA HEASARC .

The Public Use Microdata Samples (PUMS) are computer-accessible files containing records for a sample of housing Units, with information on the characteristics of each housing Unit and the people in it for 1940-1990. Within the limits of sample size and geographical detail, these files allow users to prepare virtually any tabulations they require. Each datafile is documented in a codebook containing a data dictionary and supporting appendix information. Electronic versions for the codebooks are only available for the 1980 and 1990 datafiles. Identifying information has been removed to protect the confidentiality of the respondents. PUMS is produced by the United States Census Bureau (USCB) and is distributed by USCB, Inter-university Consortium for Political and Social Research (ICPSR), and Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset contains expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity.

Real spacecraft and curiosity rover anomalies for anomaly detection

Indications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies)

SMAP Anomalies

• TM Channels (55)
• Total TM values (429,735)
• Total anomalies (69)

MSL:

TM Channels (27) Total TM values (66,709) Total anomalies (36)

Data in .npy files

All credits go to the original authors of the dataset, many thanks to them for making such data publicly available: - Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, Tom Soderstrom. Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding, 2018, NASA Jet Propulsion Laboratory - Read more of NASA anomaly detection work: https://github.com/khundman/telemanom

NASA

## Overview

NASA is a dataset for instance segmentation tasks - it contains Water annotations for 271 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Public data set for NASA Agency Intellectual Property (IP). The distribution contains both Patent information as well as General Release of Open Source Software.

A collection of downscaled climate change projections, derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth Exchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and LOCA (CMIP5).

A set of Li-ion batteries were run through different operational profiles (charge, discharge and impedance) at various temperatures. Impedance measurement was carried out through an electrochemical impedance spectroscopy (EIS) frequency.

Repeated charge and discharge cycles result in accelerated aging of the batteries while impedance measurements provide insight into the internal battery parameters that change as aging progresses. The experiments were stopped when the batteries reached end-of-life (EOL) criteria. These datasets can be used for the prediction of both remaining charge (for a given discharge cycle) and remaining useful life (RUL). Data are in Batch of 6 experiments, data provided in .mat files with experiment details in associated READEME.txt -

Dataset

This dataset was created by kushagra

Contents

The NTRS is a valuable resource for researchers, students, educators, and the public to access NASA's current and historical technical literature and engineering results. Over 500,000 aerospace-related citations, over 200,000 full-text online documents, and over 500,000 images and videos are available. NTRS content continues to grow as new scientific and technical information (STI) is created or funded by NASA. The types of information found in the NTRS include: conference papers, journal articles, meeting papers, patents, research reports, images, movies, and technical videos. NTRS is Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) enabled

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IMPORTANT

For further information, please refer to the latest version of the MODIS Fire Users Guide which can be found via the FIRMS FAQ section (https://earthdata.nasa.gov/faq#ed-firms-faq).

Please note that there is MODIS data missing from several of the data sets. There is data missing from end of June to the beginning of July in 2001, 2002 is missing some data throughout the data set, 2007 has some missing data from mid August and data is missing for part of 21 April 2009, and missing for 22 April 2009. There might also be some erroneous data present in the data set.

Please refer to the disclaimer below.

For a list of attribute fields for the MODIS data: https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/c6-mcd14dl#ed-firms-attributes

For a list of attribute fields for the VIIRS data: https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/v1-vnp14imgt#ed-viirs-375m-attributes

For the key differences between the NRT and standard products visit:
https://earthdata.nasa.gov/faq/firms-faq#ed-nrt-standard.

The MODIS and VIIRS fire files are split to ensure users clearly distinguish between these two data sources. Should you wish to combine the datasets you will still be able to distinguish the source using the Collection / Version field.

Please note: If your request results in no fire points, the accompanying ZIP file will include an empty CSV file with a header, or an empty DBF file. If you believe that this has occurred due to an error, please contact us at support@earthdata.nasa.gov.

Visit the NASA FIRMS project website at http://earthdata.nasa.gov/data/nrt-data/firms

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PROJECTION INFORMATION

The MODIS and VIIRS fire/hotspot data supplied to you are in the WGS84 Geographic projection (the "latitude/longitude projection").

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DATA CITATION AND DISCLAIMER

NASA promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public. Read the NASA Data and Information Policy.

If you provide the LANCE / FIRMS data to a third party, we request you follow the guidelines in the citation and replicate or provide a link to the disclaimer.

CITATION See: https://earthdata.nasa.gov/earth-observation-data/near-real-time/citation#ed-firms- citation

DISCLAIMER The LANCE system is operated by the NASA/GSFC Earth Science Data and Information System (ESDIS). The information presented through LANCE, Rapid Response, GIBS, Worldview, and FIRMS are provided "as is" and users bear all responsibility and liability for their use of data, and for any loss of business or profits, or for any indirect, incidental or consequential damages arising out of any use of, or inability to use, the data, even if NASA or ESDIS were previously advised of the possibility of such damages, or for any other claim by you or any other person. ESDIS makes no representations or warranties of any kind, express or implied, including implied warranties of fitness for a particular purpose or merchantability, or with respect to the accuracy of or the absence or the presence or defects or errors in data, databases of other information. The designations employed in the data do not imply the expression of any opinion whatsoever on the part of ESDIS concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. For more information please contact Earthdata Support.

This dataset was created as a data source for machine-learning models used to disambiguate acronyms with multiple definitions. This dataset includes files that cover 406,005 abstracts. 484 acronyms with multiple definitions and multiple examples of use in different abstracts were extracted.

This was found to be a suitable dataset for training disambiguation models that use the context of the surrounding sentences to predict the correct meaning of the acronym. The prototype machine-learning models created from this dataset have not been released.

The NASA Science Technology and Information Program (https://www.sti.nasa.gov/) provided the NASA Office of the Chief Information Officer Transformation and Data Division Data Analytics team with a large JSONL of public abstracts from NASA authored papers and reports. These can be found in the results_merged.jsonl. These documents were exported in late 2018 and processed in 2019. They should not be thought to be extensive or complete of all public NASA abstracts. Please contact https://www.sti.nasa.gov/ if you want a full and up-to-date data dump. This dataset is processed for a specific purpose at a specific point in time.

JSONL is used as the format instead of JSON as it is faster and easier to access specific lines without having to check the dictionary for each metadata instance.

This dataset could be used for various purposes including lists of acronyms, lists of acronym definitions, and natural language processing models to disambiguate the meanings of acronyms with more than one definition. Anthony Buonomo, Jack Steilberg, and Justin Gosses contributed preparing this dataset as part of an intern project.

License: Public Domain

IRSA is chartered to curate the calibrated science products from NASAs infrared and sub-millimeter missions, including five major large-area/all-sky surveys. IRSA data sets are cited in about 10% of astronomical refereed papers. IRSA offers access to digital archives through powerful query engines, including VO-compliant interfaces, and offers unique tools such as the IRAS scan processing tool Scanpi. IRSA exploits a re-useable architecture to deploy cost-effective archives for customers, including: the Spitzer Space Telescope; the 2MASS and IRAS all-sky surveys; and multi-mission datasets such as COSMOS. In the near future, IRSA will serve public data from the WISE all-sky survey and the Planck mission.

NASA Tweets

Analyzing Engagement Patterns

By Twitter [source]

About this dataset

This dataset provides a comprehensive look at how one of the world’s most influential space exploration agencies utilizes Twitter to communicate and interact with their followers. With over 20 million NASA followers, the organization has quickly become trustworthy in providing the public with accurate information about space exploration, satellite data, and educational opportunities. This dataset includes a wide variety of metrics to understand media usage for each tweet, reactions from users regarding NASA's tweets via likes and retweets, as well as providing insight into any outlinks associated with each tweet. It features more than 4000 individual tweets from NASA from 2015-2017 that have been carefully selected for analysis. Captured within this study are pertinent attributes related to how humans interact – both emotionally and cognitively – with technology in an increasingly digital age

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to the dataset 'NASA Tweets: Likelihood, Media Usage, and Outlink Engagement'! This dataset contains over 3 million tweets from the official Twitter account of NASA. With this data, you can examine a variety of information to better understand how people interact with and engage with messages from NASA through social media platforms.
Using this dataset, you can analyze data related to tweets including the likelihood of being retweeted or favorited along with featured media usage and outlink engagements associated with each tweet. Additionally, there is summary data available for each tweet related to when it was posted (month/year), how many users were tagged in the tweet, its sentiment expression type, sentiment score and magnitude score among other variables.

Research Ideas

• Identifying key influencers and the topics they are most passionate about in order to create tailored messaging campaigns.
• Analyzing the structure, reach, effectiveness, and sentiment of certain keywords or hashtags to develop a more effective social media strategy.
• Exploring campaigns that have been particularly successful by analyzing the rate at which people are engaging with content and outlinks associated with NASA tweets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Twitter.

Metadata relating to the FAO / NASA Harvest Public Crop Type Datasets dataset.

Meteorite Landings DatasetThe Meteoritical Society collects data on meteorites landings. This dataset, taken from public available NASA datasets, includes the location, mass, composition, and fall year for over 45,000 meteorites that have struck our planet.Dataset Variables:name: name given to meteoriteid: unique identifier given to meteoritenametype: Valid (typical meteorite) or Relict (meteorite that has been highly degraded by weather on Earth)recclass: classification given to meteoritemass (g): mass of meteorite, in gramsfall: Fall (meteorite's fall was observed) or Found (meteorite's fall was not observed)year: the year the meteorite fell or year it was foundreclat: latitude of meteorite's landingreclong: longitude of the meteorite's landingGeoLocation: a parentheses-enclose, comma-separated tuple that combines reclat and reclong

GRIN is a collection of over a thousand images of significant historical interest scanned at high-resolution in several sizes. This collection is intended for the media, publishers, and the general public looking for high-quality photographs.

The NASA Lessons Learned system provides access to official, reviewed lessons learned from NASA programs and projects. These lessons have been made available to the public by the NASA Office of the Chief Engineer and the NASA Engineering Network. Each lesson describes the original driving event and provides recommendations that feed into NASA’s continual improvement via training, best practices, policies, and procedures.

Metadata relating to the FAO / NASA Harvest Public Cropland Maps dataset.