The databases of computational and experimental data from the first Aeroelastic Prediction Workshop are located here. The databases file names tell their contents by configuration, angle of attack, Mach number and Reynolds number where necessary. The experimental data sets are in files with _X in the name. Files without an _X are computational results. These are the files updated to include data received as of Sept 11, 2013. (JH)

This web page leads to a database of images and information about the 150 major impact craters on Ganymede and is updated semi-regularly based on continuing analysis of Voyager 2 images.

Our primary goal is to automatically analyze textual reports from the Aviation Safety Reporting System (ASRS) database to detect/discover the anomaly categories reported by the pilots, and to assign each report to the appropriate category/categories. We have used two state-of-the-art models for text analysis: (i) mixture of von Mises Fisher (movMF) distributions, and (ii) latent Dirichlet allocation (LDA) on a subset of all ASRS reports. The models achieve a reasonably high performance in discovering anomaly categories and clustering reports. Each category is represented by the most representative words with the highest probability in this category. In addition, since the inference algorithm for LDA was somewhat slow, we have developed a new fast LDA algorithm which is 5-10 times more efficient than the original one, therefore more applicable for the practical use. Further, we have developed a simple visualization tool based on non-linear manifold embedding (ISOMAP) to generate a 2-d visual representation of each report based on its content/topics, which gives a direct view of the structure of the whole dataset as well as the outliers.

The JPL Small-Body Database Browser provides data for all known asteroids and many comets. Newly discovered objects and their orbits are added on a daily basis. Discovery circumstances are updated on a roughly monthly interval. Physical parameters, other than magnitude parameters, are updated on a less frequent basis.

The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science.

Use this search engine to generate custom tables of orbital and/or physical parameters for all asteroids and comets (or a specified sub-set) in our small-body database. If this is your first time here, you may find it helpful to read our tutorial. Otherwise, simply follow the steps in each section: 'Search Constraints', 'Output Fields', and finally 'Format Options'. If you want details for a single object, use the Small Body Browser instead.

The MoonDB is a database of past and present spacecraft in cislunar space, compiled through publicly available sources. It was designed to better understand the rationale behind missions, focusing on their final results, current status, funding agencies and nations. Particular attention has also been given to the surface of the Moon, where landing sites have been identified. The database is mainly based on data collected by NASA in the Master Catalogue of the NASA Space Science Data Coordinated Archive (NSSDCA) [1]. Other sources are also considered, such as the Satellite Catalog (SATCAT) from the Space-Track project [2], created by the US Combined Force Space Component Command (CFSCC). The work by McDowell (2020) [3] has also been considered an inspiration for this work, although the primary source of information has remained the NASA catalogue.

Some structural and logical changes have been introduced to follow the needs of this research project. Following a list provided by the NSSDCA, a certain number of tentative USSR missions were added to the statistics [4]. Most spacecraft were destroyed due to a launch failure and were not disclosed to the public: the available information results from an investigation.

Information for how to cite the MTE bundle.

"OpenNGC is a database containing positions and main data of NGC (New General Catalogue) and IC (Index Catalogue) objects. Unlike other similar databases which are released with license limitations, OpenNGC is released under CC-BY-SA-4.0 license, which allows the use for a wider range of cases.https://github.com/mattiaverga/OpenNGC Data SourcesOpenNGC has been built by merging data from: NASA/IPAC Extragalactic Database http://ned.ipac.caltech.edu/ This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.HyperLEDA database http://leda.univ-lyon1.fr We acknowledge the usage of the HyperLeda database (http://leda.univ-lyon1.fr)SIMBAD Astronomical Database http://simbad.u-strasbg.fr/simbad/ This research has made use of the SIMBAD database, operated at CDS, Strasbourg, FranceHEASARC High Energy Astrophysics Science Archive Research Center http://heasarc.gsfc.nasa.gov/ We used several databases from HEASARC such as mwsc, lbn, plnebulae, lmcextobj and smcclustrs."

The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.

During the final months of 2MASS observatory operations, a campaign of targeted "long exposure" observations was carried out during times when no previously unscanned parts of the sky were available for the main survey. These observations used the same freeze-frame scanning technique employed for the survey, but with READ2-READ1 exposures six times longer than was used for normal survey observations (hence they are referred to as "6x" observations). The 2MASS 6x measurements were intended to probe ~1 magnitude deeper than the main survey in unconfused regions.Approximately 590 deg2 of sky distributed in 30 targeted regions were scanned at least once using the long exposures. Most of this area is concentrated in two large, comprehensive surveys of the Large and Small Magellanic Clouds, 383 deg2 and 127 deg2, respectively. Twenty-eight additional smaller fields were mapped in the 6x mode from both observatories, covering targets that include the Pleiades open cluster, galactic star formation complexes, M31, nearby galaxy clusters and the Lockman Hole. Data processing produced a 6x Image Atlas and 6x point and extended source Working Databases (6x-PSWDB and 6x-XSWDB), analogous to those from the main survey. "Catalogs" of point and extended source detections (6x-PSC and 6x-XSC) that represent uniform, higher reliability single-epoch snapshots of the near infrared sky were drawn from the 6x WDBs using SNR and quality criteria similar to those used to construct the All-Sky Release PSC and XSC (A3.6.c). The 6x-PSC and 6x-XSC have not received the same level of scrutiny and validation as the 2MASS All-Sky PSC and XSC, though.Unlike the All-Sky Release Catalogs, the 6x Catalogs are not released as separate tables. The 6x Point and Extended Source Catalogs are instead integrated into the respective 6x Point and Extended Source WDBs. Sources comprising the Catalogs are denoted in the WDBs with the cat flag, and have cat="1".

The merged source tables contain the mean positions magnitudes and uncertainties for sources detected multiple times in each of the 2MASS data sets. The merging was carried out using an autocorrelation of the respective databases to identify groups of extractions that are positionally associated with each other, all lying within a 1.5" radius circular region. A number of confirmation statistics are also provided in the tables that can be used to test for source motion and/or variability, and the general quality of the merge.

This web page leads to a database of images and information about the 150 major impact craters on Callisto and is updated semi-regularly based on continuing analysis of Voyager images.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

NED Cone Search service (search for objects Near Position). This service searches NED's master list of extragalactic objects for entries near a given position.

The database consists of a survey of NASA Interplanetary Missions (NIMs) launched between May 1989 and October 2023. Mission data includes system-level attributes in support of advanced system-level modeling. The attributes include elements of the program, schedule, cost, and technical design. Cost silos include three inflation formats: Then$ representing realized expenses in the Year of recording, NASA’s New Start Inflation Index (NSII) adjusted expenses, and Personal Consumption Expenditures Price Index (PCEPI) adjusted expenses. Inflated values are adjusted to fiscal Year 2022 equivalent dollars with an index of 1.00 applied forward to adjust to 2023 and 2024 equivalent expenditures. The NIM database serves as a reference for future research and cost analytics in NASA's system-level analysis of the NIM., , # NASA interplanetary mission (NIM) dataset

Dataset DOI: 10.5061/dryad.fj6q5745z

Description of the data and file structure

The database consists of a survey of NASA Interplanetary Missions (NIMs) launched between May 1989 and October 2023. Mission data includes system-level attributes in support of advanced system-level modeling. The attributes include elements of the program, schedule, cost, and technical design. Cost silos include three inflation formats: Then$ representing realized expenses in the Year of recording, NASA’s New Start Inflation Index (NSII) adjusted expenses, and Personal Consumption Expenditures Price Index (PCEPI) adjusted expenses. Inflated values are adjusted to fiscal Year 2022 equivalent dollars with an index of 1.00 applied forward to adjust to 2023 and 2024 equivalent expenditures. The NIM database serves as a reference for future research and cost analytics in NASA's system-level analysis of the Interplanetary Mission.

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description: The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag GLC in the landslide editor. This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fmaps.nccs.nasa.gov%2Fserver%2Frest%2Fservices%2Fglobal_landslide_catalog%2Fglc_viewer_service%2FFeatureServer&source=sd. To export GLC data, you must agree to the Terms and Conditions . We request that anyone using the GLC cite the two sources of this database: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2]; abstract: The Global Landslide Catalog (GLC) was developed with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 at NASA Goddard Space Flight Center. This is a unique data set with the ID tag GLC in the landslide editor. This dataset on data.nasa.gov was a one-time export from the Global Landslide Catalog maintained separately. It is current as of March 7, 2016. The original catalog is available here: http://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fmaps.nccs.nasa.gov%2Fserver%2Frest%2Fservices%2Fglobal_landslide_catalog%2Fglc_viewer_service%2FFeatureServer&source=sd. To export GLC data, you must agree to the Terms and Conditions . We request that anyone using the GLC cite the two sources of this database: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561575. doi:10.1007/s11069-009-9401-4. [1] Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. [2]

Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential “master regulators” coordinating a systemic response to microgravity and/or space radiation with TGF-β1 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-β1 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis–based research.

The Asteroid Polarimetric Database (APD) is a collection of asteroid polarimetry results compiled by D.F. Lupishko of Karazin Kharkiv National University, Ukraine. It is intended to include most asteroid polarimetry available through February 15, 2019.

The PISUNA-net Local Observations database was developed to record, archive, and share indigenous and local knowledge and expertise on natural resources and resource use on the western and northern coasts of Greenland.