Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
679
datasets available to search
ShareScore release 0.7.1
Dataset results
679 results for “retrieval”
Bangla Information Retrieval Test Collection | Revisiting Anwesha
<p>There are several IR test collections available in English (e.g. http://ir.dcs.gla.ac.uk/resources/test_collections/). Unfortunately, no Gold standard dataset existed for Bangla IR until recently (https://zenodo.org/record/6583149). Our work expands the existing Gold standard dataset by creating 100 query document relevance pairs across a new test collection of 1000 documents. The corpus contains news articles from Ebela, Zee News and Anandabazar Patrika, Vikaspedia and various Bangla travel blogs. The definition of the complexity level of a query is described below:</p> <p>Complexity Level 1: The query contains exact words, phrases or sentence from the document.</p> <p>Complexity Level 2: The query is not present as it is in the document. There is a slight deviation.</p> <p>Complexity Level 3: The query is a generalised phrase capturing the overall story or the document’s theme.</p> <p>Complexity Level 4: It is a general query not related to any specific document.</p>
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
Global net ecosystem exchange of CO2 inferred from the OCO-2 XCO2 retrievals (GCAS OCO-2 inversion)
<p>Here is a dataset of global carbon flux estimates over 2015-2019 using the OCO-2 column-averaged dry-air mole fraction (XCO<sub>2</sub>) retrievals (ACOS XCO<sub>2</sub> v10) by the global carbon assimilation system (GCAS v2) (Jiang et al., 2021). </p> <p> </p> <p><strong>Citations:</strong></p> <p>Jiang, F. et al., 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmos. Chem. Phys., 21(3): 1963-1985.</p> <p>Jiang, F. et al., 2022. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data., 14, 3013–3037.</p> <p>He, W., Jiang, F., Ju, W., et al. Improved constraints on the recent terrestrial carbon sink over China by assimilating OCO-2 XCO<sub>2 </sub>retrievals, JGR-Atmopsheres, 2022, under review.</p> <p><strong>Contacts: </strong></p> <p>Wei He (weihe@nju.edu.cn); Fei Jiang (jiangf@nju.edu.cn)</p> <p>Note: <strong>If you want to use this dataset for your researches, please contact us in advances. </strong>Thank you!</p>
NOAA NSSL CLAMPS2 TROPoe Retrievals
<p>These files contain 24-hour periods of retrieved thermodynamic profiles derived from observations collected by the CLAMPS2 Atmospheric Emitted Radiance Interferometer (AERI). These retrievals were processed in the IDL (e.g., the AERIoe version) of the TROPoe algorithm (see Turner and Loehnert 2014; Turner and Blumberg 2019). These data were collected during the SPLASH-SAIL project. Quicklook imagery included in QL.zip.</p> <p>The AERI consists of a Fourier transform interferometer, scene scanning-optics, IR detector, calibration blackbodies, and instrument control hardware. The exact system design and extensive theory of operation can be found in Knuteson et al. (2004). On a clear sky day it is capable of measuring IR radiances throughout the depth of the atmosphere with a wavenumber resolution 1 cm<sup>-1 </sup>and temporal resolution of ~20 seconds. The AERI has an absolute accuracy of < 1% of the ambient blackbody radiance and has typical noise < 0.2 mW ( m<sup>2</sup>sr cm<sup>-1</sup>)<sup> -1</sup>. The instrument is not able to collect observations in precipitation and when precipitation is detected a mechanical hatch is closed to protect the instrument. The radiances collected by the AERI contain information that can be used to obtain profiles of temperature, water vapor and trace gases as well as basic cloud properties. Complete detail included in the README file.</p>
Relationships among the generated content type, task, AI technique, and application domain in the retrieved works
<p>Relationships among the generated content type, task, AI technique, and application domain in the retrieved works. Part of the study "What do we mean by GenAI?"</p>
Touché23-Image-Retrieval-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef23/touche23-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2023.</p> <p>This version is lacking the touche23-image-search-archives.zip and touche23-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-23/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-23/</a></p> <p> </p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Differentiation of functional networks during long-term memory retrieval in children and adolescents
Open the record for dataset details and reuse information.
Neural Differentiation Tracks Improved Recall of Competing Memories Following Interleaved Study and Retrieval Practice
Open the record for dataset details and reuse information.
Retrieving Affected Versions by Leveraging the Life Cycle of Defects
<p>This is the online appendix for our paper submission entitled "Retrieving Affected Versions by Leveraging the Life Cycle of Defects"</p>
Metagenomics assemblies and high-quality MAGs for "Long-read metagenomics to retrieve high-quality metagenome-assembled genomes from canine feces"
<p>This dataset includes the different metagenomics assemblies analyzed and its summary (_info.txt file):</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/100_assembly.fasta">100_assembly.fasta</a> is the Flye 2.7 metagenomics assembly merging HMW and non-HMW datasets</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/75_assembly.fasta">75_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 75% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/50_assembly.fasta">50_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 50% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/HMW_assembly.fasta?versionId=749ff6fd-2642-4ad1-971a-7f3404baa595">HMW_assembly.fasta</a> is the Flye 2.7 metagenomics assembly for HMW dataset.</p> <p>Moreover, it also includes the eight frameshift-corrected high-quality MAGs analyzed in the manuscript. </p>
AILA 2019 Precedent & Statute Retrieval Task
<p><strong>Dataset of the AILA (Artificial Intelligence for Legal Assistance) Track at FIRE 2019</strong></p> <p>Track website : https://sites.google.com/view/fire-2019-aila/<br> Conference website : http://fire.irsi.res.in/fire/2019/home</p> <p>In<strong> </strong>countries following the Common Law system (e.g., UK, USA, Canada, Australia, India), there are two primary sources of law – <em>Statutes</em> (established laws) and <em>Precedents</em> (prior cases). Statutes deal with applying legal principles to a situation (facts / scenario / circumstances which lead to filing the case). Precedents or prior cases help a lawyer understand how the Court has dealt with similar scenarios in the past, and prepare the legal reasoning accordingly.</p> <p>When a lawyer is presented with a situation (that will potentially lead to filing of a case), it will be very beneficial to him/her if there is an automatic system that identifies a set of related prior cases involving similar situations as well as statutes/acts that can be most suited to the purpose in the given situation. Such a system shall not only help a lawyer but also benefit a common man, in a way of getting a preliminary understanding, even before he/she approaches a lawyer. It shall assist him/her in identifying where his/her legal problem fits, what legal actions he/she can proceed with (through statutes) and what were the outcomes of similar cases (through precedents).</p> <p>Motivated by the above scenario, we propose two tasks here :</p> <ul> <li><strong>Task 1 : Identifying relevant prior cases for a given situation</strong></li> <li><strong>Task 2 : Identifying most relevant statutes for a given situation</strong></li> </ul> <p> </p> <p><strong>Task Description:</strong></p> <p>You will be given a set of 50 queries, each of which describes a situation.</p> <p><em><strong>Task 1: Identifying relevant prior cases</strong></em></p> <p>We provide ~3000 case documents of cases that were judged in the Supreme Court of India. For each query, the task is to retrieve the most similar / relevant case document with respect to the situation in the given query.</p> <p><em><strong>Task 2: Identifying relevant statutes</strong></em></p> <p>We have identified a set of 197 statutes (Sections of Acts) from Indian law, that are relevant to some of the queries. We provide the title and description of these statutes. For each query, the task is to identify the most relevant statutes (from among the 197 statutes). Note that, the task can be modelled either as an unsupervised retrieval task (where you search for relevant statues) or as a supervised classification task (e.g., trying to predict for each statute whether it is relevant). For the latter, case documents provided for Task 1 can be utilised. However, if a team wishes to apply supervised models, then it is their responsibility to create the necessary training data.</p>
Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals
<p>We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. </p><p>airqual.qc.csv includes assimilated and verified surface NO2 observations.</p><p>HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.</p><p>posterior_emission_27km.nc and posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.</p>
Source Data for Manuscript: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs"
<p>This dataset accompanies the manuscript entitled: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs", which was accepted for publication in the Planetary Science Journal. Included are the source files for all figures included in the paper.</p>
Experimental data for "Exact inversion of partially coherent dynamical electron scattering for picometric structure retrieval"
Open the record for dataset details and reuse information.
Measuring semantic memory using associative and dissociative retrieval tasks
<p>Recent theoretical advances highlighted the need for novel means of assessing semantic cognition. Here, we introduce the Associative-Dissociative Retrieval Task (ADT), positing a novel way to test inhibitory control over semantic memory retrieval by contrasting the efficacy of associative (automatic) and dissociative (controlled) retrieval on standard set of verbal stimuli. All ADT measures achieved excellent reliability, homogeneity, and short-term temporal stability. Moreover, in-depth stimulus level analyses showed that associating is easier for words evoking few but strong associates, yet such propensity hampers the inhibition. Finally, we provided critical support for the construct validity of the ADT measures, demonstrating reliable correlations with domain-specific measures of semantic memory functioning (semantic fluency and associative combination) but negligible correlations with domain-general capacities (processing speed and working memory). Together, we show that ADT provides simple yet potent and psychometrically sound measures of semantic memory retrieval and offers noteworthy advantages over the currently available assessment methods.</p>
Zebra finch dataset for the paper: Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development
<p>This is the dataset created in the paper "Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development".</p>
Touché25-Image-Retrieval-and-Generation-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef25/touche25-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2025.</p> <p> </p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/</a></p>
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank
Training and test data for retrievals based on HATPRO observations during MOSAiC
<p>The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of temperature (ta) and humidity (hua) profiles, integrated water vapour (prw), and liquid water path (clwvi) from brightness temperatures (tb) measured by a HATPRO (humidity and temperature profiler). A regression with quadratic terms, except for the boundary layer scan which is confined to linear terms, is performed to derive these meteorological variables. The trained retrieval is applied on the HATPRO observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on Ny-Ålesund radiosonde observations. An IDL-based radiative transfer model has been used to simulate brightness temperatures for each radiosonde. Several elevation angles (ele) are given in the file because the HATPRO radiometer performs elevation scans in between zenith scans to increase the resolution of temperature profiles in the atmospheric boundary layer.</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.