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.
33
datasets available to search
ShareScore release 0.7.1
Dataset results
33 results for “information retrieval”
The Role of Automated Categorisation in e-Government Information Retrieval
<p>High-precision search results are essential for helping e-government employees complete work-based tasks. Prior studies have shown that existing features of e-government systems need improvement in terms of search facilities, navigation, and metadata adoption. This paper investigates how automated categorization can enhance information organization and retrieval and presents the results of a controlled evaluation that compared automated categorization and free text indexing of the government intranet used by Danish tax authorities. Thirty-two individuals participated in the evaluation, conducting simulated searches and genuine search tasks. Searching behaviour and search outcome was documented by search logs, relevance assessments, and post search interviews.</p> <p>The evaluation demonstrates a high potential for automated categorization in a government context. Overall, the categorized organization generated more reformulations and less query success. Session success was found to be fairly even between the two systems. Qualitative data revealed that categorized overviews of the search results were useful if the participating employee did not possess extensive knowledge of the task at hand. When task knowledge was present, categorization was used to support the assumptions of a correct search. On the other hand, however, test participants avoided using automated categorization if high-precision documents were among the top results or if few documents were retrieved. The findings emphasize the importance of simultaneously providing different search options for e-government IR systems and reveal that automated categorization is a valuable candidate for improving search facilities within this domain.</p>
Data from: Test collections for EHR-based clinical information retrieval
Open the record for dataset details and reuse information.
OCO-3 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information, Retrospective Processing V10r (OCO3_L2_Diagnostic) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
OCO-2 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information Retrospective Processing V11.2r (OCO2_L2_Diagnostic) at GES DISC
Version 11.2r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.
OCO-2 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information, Retrospective Processing V10r (OCO2_L2_Diagnostic) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.In early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
OCO-2 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information, Retrospective Processing V11r (OCO2_L2_Diagnostic) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
OCO-3 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information, Retrospective Processing V11r (OCO3_L2_Diagnostic) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
(Inter)subjectivity and information structure dataset: topics retrieved from CallFriend Corpus
<p>Aboutness and framing topic at the left and right periphery of the Mandarin uttterance. Data extracted from Callfriend Corpus. Code available upon request.</p>
OCO-3 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information, Forward Processing V11 (OCO3_L2_Diagnostic) at GES DISC
Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
OCO-3 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information, Forward Processing V10 (OCO3_L2_Diagnostic) at GES DISC
Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
OCO-2 Level 2 geolocated XCO2 retrieval results and algorithm diagnostic information V11.2 (OCO2_L2_Diagnostic) at GES DISC
Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass various data fields used for diagnostic and pre-processing, including aerosol optical depth, albedo, absorption coefficients, fluorescence, XCO2 uncertainties, averaging kernel, surface type, etc.
Retrieval of Patient Information After Discontinuation
ClinicalTrials.gov study NCT01658722. IPD Sharing: Not stated. Countries: 0. Publications: 0.
3R information retrieval self-reports by aspiring directors of animal experimentation
<p>This dataset (qualitative data) consists in 215 anonymized pdfs of 3R information retrieval self-reports completed by aspiring directors of animal experimentation before 5 educational sessions on information retrieval (2012 to 2015). Visible confidential data, name, address, institution or industry, lab name, email, phone numbers were suppressed with Adobe Pro XI Redaction tool, as well hidden data, such as metadata with sanitization tool.</p> <p>In 2012, 2013, 2014, 2015, aspiring directors of animal experimentation were assigned a 3R information retrieval homework prior attending a mandatory 3 hours face-to-face information retrieval seminar. Participants had to send back self-reports to course organizer of the training module (Module-2), the one week mandatory course for aspiring directors of animal experimentation located in Switzerland. In 2015, before performing self-report, aspiring directors made an online test about basics of information retrieval, 3Rs, and open access. They should obtain 60% of right answers and forwarded the automatic generated certificate to course organizer. Researchers could answer the questions with their personal knowledge or find them in commented presentations and screen castings available on 3Rupdate.ch plateform.</p> <p>Homework were not designed originally to analyze information retrieval skills. The original goal for the homework was to draw more attention and retention from the researchers during the 3 hours seminar. It was thought that if researchers faced difficulties in searching 3R information, it would generate additional motivation for the course. Therefore, self-reports were not marked; no direct individual feed-back was given to the participants, but the face-to-face session was a kind of homework correction, a way to give tips about 3R information retrieval in order to improve their searches. The second goal was to allow researchers to think about how they selected and handled search tools, instead of relying on closely related peers and personal knowledge to gather information on the research subject, an approach that is well known and natural: with time, researchers rely much more on their own network of peers rather than on online information resources to follow the latest development in their field.</p> <p>The homework topic was related to study model selection. The choice of a study model is crucial from a 3R point of view, but also for later scientific result validity: it is not an easy task for fundamental researchers to follow new animal models available on market. Neither is the one to follow new in vitro alternatives, such as 3D tissue cultures, stem cells, and <em>in vitro</em> screening techniques for fundamental researchers using animal experimentation.</p> <p>The case study was: Which mice, or non-vertebrate, or in vitro models are used to study Huntington disease?</p> <p>Homework structure consisted of a brief theory on 3R information retrieval, suggested search tools to be used, and 3 assignments. The first assignment was to perform a search on the subject, the second one to report best searches, namely keywords and search strings used in two selected tools with corresponding relevant references and generated bibliography. The third assignment invited researchers to comment freely about their tool choices, tool differences, and report self-criticism on search strategy, submitting better formulation of the initial query for a better information retrieval process. </p> <p> </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.