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.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “Algorithm Bias”
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm
<p>Data of investigation published in the article Cristòfol Rovira; Lluís Codina; Carlos Lopezosa Language Bias in the Google Scholar Ranking Algorithm. Future Internet, 2021, 13.</p> <p><strong>Abstract: </strong>The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.</p>
The Choice is Yours? How Algorithm Bias Impacts Fairness and Accessibility of Knowledge
<p><strong>Episode Summary</strong></p> <p>In this episode we talked about 'almighty' algorithms with Carlos Castillo, Lorenzo Porcaro, Marzieh Karimihaghighi, David Solans, and Francesco Fabbri from the Web Science & Social Computing Research Group, and the department of Engineering in Information & Communication Technologies, in Universitat Pompeu Fabra in Barcelona. We discussed how bias can enter into algorithm systems, how bias is measured, and what systems are impacted by it. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.upf.edu/web/wssc/">Web Science and Social Computing Research Group</a></p> <ul> <li><a href="https://www.upf.edu/web/etic/entry/-/-/24095/adscripcion/carlos-alberto-alejandro-castillo">Carlos Castillo</a></li> <li><a href="https://www.linkedin.com/in/marzieh-karimihaghighi-706b5554/?originalSubdomain=ir">Marzieh Karimihaghighi</a></li> <li><a href="https://www.linkedin.com/in/david-solans-noguero-48269b85/?originalSubdomain=es">David Solans</a></li> <li><a href="https://www.linkedin.com/in/francesco-fabbri/?originalSubdomain=it">Francesco Fabbri</a></li> <li><a href="https://www.linkedin.com/in/lorenzo-porcaro-7a8792b1/?originalSubdomain=es">Lorenzo Porcaro</a></li> </ul>
Dataset for "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets"
<p>This dataset is associated with a research article entitled "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets".</p>
Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK
Open the record for dataset details and reuse information.
Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
<p>Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Other bias types however have received lesser amounts of scrutiny. This work describes a large-scale analysis of sentiment associations in popular word embedding models along the lines of gender and ethnicity but also along the less frequently studied dimensions of socioeconomic status, age, physical appearance, sexual orientation, religious sentiment and political leanings. Consistent with previous scholarly literature, this work has found systemic bias against given names popular among African-Americans in most embedding models examined. Gender bias in embedding models however appears to be multifaceted and often reversed in polarity to what has been regularly reported. Interestingly, using the common operationalization of the term bias in the fairness literature, novel types of so far unreported bias types in word embedding models have also been identified. Specifically, the popular embedding models analyzed here display negative biases against middle and working-class socioeconomic status, male children, senior citizens, plain physical appearance and intellectual phenomena such as Islamic religious faith, non-religiosity and conservative political orientation. Reasons for the paradoxical underreporting of these bias types in the relevant literature are probably manifold but widely held blind spots when searching for algorithmic bias and a lack of widespread technical jargon to unambiguously describe a variety of algorithmic associations could conceivably be playing a role. The causal origins for the multiplicity of loaded associations attached to distinct demographic groups within embedding models are often unclear but the heterogeneity of said associations and their potential multifactorial roots raises doubts about the validity of grouping them all under the umbrella term bias. Richer and more fine-grained terminology as well as a more comprehensive exploration of the bias landscape could help the fairness epistemic community to characterize and neutralize algorithmic discrimination more efficiently.</p>
Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
Open the record for dataset details and reuse information.
Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias
Open the record for dataset details and reuse information.
Data from: An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes
Open the record for dataset details and reuse information.
OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11r (OCO2_L2_Lite_SIF) 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 OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. 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 the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.
OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11.2r (OCO2_L2_Lite_SIF) at GES DISC
Version 11.2r is the current version of the data set. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. 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 the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.
OCO-3 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Forward Processing V11 (OCO3_L2_Fwd_SIF) 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 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V10r (OCO3_L2_Lite_SIF) 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 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V10r (OCO2_L2_Lite_SIF) 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 OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. 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 the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.
A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem
<p>The dataset for our paper 'A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem'. It consists of 5 real KGs (<code>Mondial</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, <code>DBpedia</code>) and 5 synthetic KGs (<code>LUBM-10U</code>, <code>LUBM-50U</code>, <code>LUBM-250U</code>, <code>LUBM-2U</code>, <code>DBP-50K</code>). Each KG is compressed in one file, which including (for example, in <code>LUBM-2U</code>):</p> <ul> <li> <p><code>lubm_2u_nodes.sql</code>: the id, the name and the weight of a node,</p> </li> <li> <p><code>lubm_2u_edges.sql</code>: the ids of two nodes an edge connects,</p> </li> <li> <p><code>lubm_2u_queries.sql</code>: a query consists of some keywords,</p> </li> <li> <p><code>lubm_2u_keymap.sql</code>: a keyword maps to a set of nodes,</p> </li> <li> <p><code>lubm_2u_nodevec.sql</code>: the vector of a node, used to compute quadratic function qw,</p> </li> <li> <p><code>lubm_2u_hub_hop.sql</code>: the hub labeling index to compute in Section 4.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_1.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_5.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.5,</p> </li> <li> <p><code>lubm_2u_hub_mix_9.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.9.</p> </li> </ul> <p>You can dump the data into MySQL database. For example,</p> <pre><code>create database lubm_2u; use lubm_2u; source lubm_2u_nodes.sql; …</code></pre> <p>Unfortunately, due to the limit of space, for large KGs (<code>DBpedia</code> and <code>LUBM-250U</code>), we don't directly provide the data of hub labeling, i.e., these two compressed files only contains the first 5 sql files. You should generate hub labeling by yourself where the process is detailed in <a href="https://github.com/nju-websoft/QGSTP-HB/blob/main/README.md">README</a> of our project.</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.