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131 results for “queries”
Webis Query Segmentation Corpus 2010 (Webis-QSeC-10)
<p>The Webis Query Segmentation Corpus 2010 (Webis-QSeC-10) contains segmentations for 53,437 web queries obtained from Mechanical Turk crowdsourcing (4,850 used for training in our CIKM 2012 paper). For each query, at least 10 MTurk workers were asked to segment the query. The corpus represents the distribution of their decisions.</p> <p>We provide the training and test sets as single folders in Zip archives containing several files. The files "...-queries.txt" contain the query strings and a unique ID for each query. The files "...-segmentations-crowdsourced.txt" contain the crowdsourced segmentations with their number of votes per query ID (see below for an example). The "data" folders contain all the data (n-gram frequencies, PMI values, POS tags, etc.) needed to replicate the evaluation results of our proposed segmentation algorithms. For convenience reasons, the folder "segmentations-of-algorithms" contain the segmentations that our proposed algorithms compute.</p> <p>The original queries were extracted from the AOL query log, and range from 3 to 10 keywords in length. For each query at least 10 MTurk workers were asked to segment the query and their decisions are accumulated in the corpus. The examples below demonstrate two different cases.</p> <p>Sample queries with internal ID (as in "Webis-QSeC-10-training-set-queries.txt"):</p> <ul> <li>2315313155 harvard community credit union</li> <li>1858084875 women's cycling tops</li> </ul> <p>Sample segmentations (as in "webis-qsec-10-training-set-segmentations-crowdsourced.txt"):</p> <ul> <li>2315313155 [(6, 'harvard community credit union'), (2, 'harvard community|credit union'), (1, 'harvard|community|credit union'), (1, 'harvard|community credit union')]</li> <li>1858084875 [(5, "women's|cycling tops"), (2, "women's|cycling|tops"), (2, "women's cycling|tops"), (1, "women's cycling tops")]</li> </ul> <p>Each query has a unique internal ID (e.g., 2315313155 in the first example) and the segmentations file contains at least 10 different decisions the MTurk workers made for that query. In the first example, 6 workers have all 4 keywords in one segment, 2 workers decided to break after the second word (denoted by a |) etc. Note that apostrophe in the second example (query ID 1858084875) is escaped by double quotes around the segmentation strings.</p>
Webis Query-Task-Mapping Corpus 2019 (Webis-QTM-19)
<p>The Webis Query-Task-Mapping Corpus 2019 (Webis-QTM-19) comprises three benchmark datasets on the query-task-mapping problem, which consists of finding the correct task for a new query in a given task-split background query log.</p> <p>It comprises three subdatasets in separate CSV files, each of which has three columns:</p> <ul> <li>Query. The query string.</li> <li>Source. The source of the query. In all datasets, a source field with value 'google' or 'bing' indicates that the query was derived from query suggestions from the respective search engine; otherwise, the query is from one of the underlying base corpora: <ul> <li>'lucc' : lucchese:2011</li> <li>'webis' : stein:2013b</li> <li>'trc' : stein:2016a</li> <li>'trec' : various collections of TREC queries</li> <li>'wikihow' : based on titles of wikiHow questions</li> </ul> </li> <li>Task. The ID of the ground-truth task for the corresponding query.</li> </ul> <p><br> Further details can be found in reference:<br> Michael Völske, Ehsan Fatehifar, Benno Stein, and Matthias Hagen. Query-Task Mapping. In 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019), July 2019. ACM.<br> http://doi.acm.org/10.1145/3331184.3331286</p>
Webis Netspeak Instant Query Log 2021 (Webis-NIL-21)
<p>The Webis Netspeak Instant Search Log 2021 (Webis-NIL-21) is an excerpt of the log of the Netspeak search engine. The dataset contains about 37,000 log entries, which correspond to keystroke interactions the users of Netspeak made with it's search interface while entering their queries. This enables the study of instant search logs in general, and that of identifying keystroke interactions belonging to the same query in particular. The latter is annotated in the log.</p>
Datasets for Creating and Querying Personalized Versions of Wikidata in a Laptop (Wikidata workshop, 2021)
<p>These datasets are used to support the results of the paper "Datasets for Creating and Querying Personalized Versions of Wikidata in a Laptop", submitted to the Wikidata workshop 2021 (https://wikidataworkshop.github.io/2021/) at the International Semantic Web Conference.</p> <p>The datasets have been derived from Wikidata dump 20210215. To help querying purposes, the dump is organized in different files:</p> <ul> <li>claims.time.tsv.gz: time-related assertions</li> <li>claims.wikibase-item.tsv-006.gz: item-related assertions</li> <li>derived.P279.tsv.gz: statements that are subclass of another statement</li> <li>derived.P279star.tsv.gz: statement that are subclass of another statement, including their chains.</li> <li>derived.P31.tsv.gz: instance of statements.</li> <li>labels.en.tsv-004.gz: labels in English</li> <li>claims.external-id.tsv-005.gz: External identifiers for each item.</li> <li>ulan.tsv: ULAN ids (used to link external identifiers to Wikidata identifiers)</li> <li>wikidata_infobox.tsv.gz: Information about dbpedia infoboxes.</li> </ul> <p>Upload by: Daniel Garijo</p>
Cache for KGTK queries in Creating and Querying Personalized Versions of Wikidata on a Laptop
<p>Sqlite cache used to store the KGTK Kypher (https://kgtk.readthedocs.io/en/dev/transform/query/) queries for paper "Creating and Querying Personalized Versions of Wikidata on a Laptop"</p>
Instagram: #erasurepoetry query
<p><strong>Tags: #erasurepoetry vs. #blackoutpoetry on Instagram (Nov. 19, 2020) </strong></p> <p>As of November 19, 2020, searching on Instagram for the tags “#erasurepoetry” and “#blackoutpoetry” shows some overlapping of techniques in both tags.</p>
The LOTUS Initiative for Open Natural Products Research: wikidata query results
<p>Wikidata query results returned by the downloadLotus module of the <a href="https://github.com/lotusnprod/lotus-wikidata-interact">https://github.com/lotusnprod/lotus-wikidata-interact</a> program.</p> <p>See details of the module here <a href="https://github.com/lotusnprod/lotus-wikidata-interact/blob/main/downloadLotus/README.md">https://github.com/lotusnprod/lotus-wikidata-interact/blob/main/downloadLotus/README.md</a></p> <p>This dataset is constituted of 4 tables.</p> <ol> <li>compounds.tsv - chemical structures metadata (wikidataId, canonicalSmiles, isomericSmiles, inchi, inchiKey)</li> <li>references.tsv - bibliographical references metadata (wikidataId, pipe separated DOIs, titles)</li> <li>taxa.tsv - biological organisms metadata (wikidataId, pipe separated names, taxa rank)</li> <li>compound_reference_taxon.tsv - the documented structure-organism pairs</li> </ol> <p>This dataset includes not only the outputs of the LOTUS processing pipeline (available here <a href="https://doi.org/10.5281/zenodo.5665295">https://doi.org/10.5281/zenodo.5665295</a> ) but also any of wikidata chemical compounds having the found in taxon property (<a href="https://www.wikidata.org/wiki/Property:P703">https://www.wikidata.org/wiki/Property:P703</a>) and their associated organisms and documenting references.</p> <p> </p> <p><br> </p>
URLs with query strings for software in UK Academic Institutional Repositories.
<p>A set of exact URLs containing the query strings to search for software within UK Academic IRs.</p>
Ranking snapshot for the query "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE" performed on Google Scholar
This deposit provides a snapshot of results obtained through a Google Scholar search query on the topic of "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE". The search was performed by Alessandro Lotta, from Unipd, on May 30, 2023 at 6:34:32 PM. The captured data includes 2 pages of search results, which have been saved in the output-data.jsonld file. In addition to the citation data, the deposit includes PNG format screenshots of the search results, allowing visual reference to the captured information. The metadata for the Research Object Crate is also included in JSON format, providing essential details about the contents. The citation snapshot presented here is generated using the Unipd Ranking Citation Tool, a tool developed by Gianmaria Silvello and Alessandro Lotta (University of Padua). This tool, accessible at https://rankingcitation.dei.unipd.it
Implementing Group Physical Therapy (PT) for Veterans With Knee Osteoarthritis (Group PT): Function QUERI 2.0
ClinicalTrials.gov study NCT05282927. IPD Sharing: YES. Countries: 1. Publications: 3.
Implementing a Hospital-Based Walking Program (STRIDE): Function QUERI 2.0
ClinicalTrials.gov study NCT04868656. IPD Sharing: YES. Countries: 1. Publications: 2.
Implementing a Skills-Based Caregiver Training Program (Caregivers FIRST): Function QUERI 2.0
ClinicalTrials.gov study NCT05319535. IPD Sharing: YES. Countries: 1. Publications: 3.
A Simple Semantic-based Data Storage Layout for Querying Point Clouds
<p>Dataset contains three datasets: SMALL, MEDIUM and LARGE point cloud.</p> <p>Contains Python code which is made up of two files pc_new_semantics.py and paperutils.py</p> <p> </p>
NCI Semantic Competency Query Review
<p><strong>Overview</strong></p> <p>NCI held a Workshop on Semantics to support the NCI Cancer Research Data Commons (CRDC) in May 2018 at the National Cancer Institute in Rockville, MD. This workshop brought together experts in various areas of semantics, data integration and harmonization, Natural Language Processing (NLP) and other relevant areas to discuss and gather recommendations on semantic support for the CRDC.</p> <p>The workshop goals were to:<br> 1) Identify high-level requirements to address semantic needs and potential approaches for evaluation testing of the Cancer Data Aggregator (CDA)</p> <p>2) A set of options for using and/or extending current methods and resources (e.g. NLP) to</p> <ul> <li> <p>support semantic query capabilities</p> </li> </ul> <ul> <li> <p>facilitate metadata annotation</p> </li> <li> <p>minimize efforts for data validation and submission</p> </li> </ul> <p>3) Develop recommendations to support ongoing engagement with the community to ensure the semantics underlying the CDA improve and evolve as people contribute to and use the CDA</p> <p><strong>Participants</strong></p> <p>In total, 33 participants attended the meeting, coming from various backgrounds including clinicians, ontologists, bioinformaticians, data scientists, and project managers. Participants had expertise in semantic technologies, software and infrastructure development, data standards, data integration, clinical research, open source tool development. </p> <p><strong>Competency Queries</strong></p> <p>At the workshop, participants were asked to brainstorm ‘competency queries’, potential queries or questions that they would ask the future Cancer Data Aggregator (CDA) in order to retrieve data from across the CRDC. At the workshop, the breakout groups documented 237 queries for the CDA. </p> <p>Competency queries are often used to inform requirements to build a data model and/or ontology. They can help inform the scope of the model:what queries should the model support; 2) the content of the model, in terms of what types of entity types and attributes are needed to answer these queries; 3) the structure of the model in terms of what types of relationships between entities are needed to efficiently answer queries; 4) the semantics of the data, meaning which terminologies/ontologies would be useful for representing data to support query needs; 5) how to test and improve a completed model to ensure it can efficiently support queries determined to be in scope.</p> <p>After the workshop, a small subgroup assessed, organized and summarized the 237 queries that were noted at the workshop. A spreadsheet was created containing the queries and the keywords in each query were highlighted. From the highlighted keywords, a column was added to capture the core search parameters or classifications for each query. In evaluating the queries it was observed that some were really not a query, but expressed various observations about the data that one might hope to make. For example “Patients with a certain temporal pattern of diagnoses, both cancer and comorbidities”. This submission indicates that the data returned would need to include diagnosis and other conditions and can help to inform the requirements for CRDC data models. </p> <p>Using the information from the keyword analysis, the queries were initially categorized across various classifications, such as queries that included exposure information, diagnosis or cancer types, anatomical location of tumor, etc. In total, the queries were classified amongst 25 different parameters, or an ‘other’ category, where the query did not fit the classification scheme, or was out of scope. To further refine this list into a more manageable list, 82 representative queries were pulled out, with at least 2 examples from every classification parameter. The goal was to identify a minimal or at least smaller subset that was still representative. This list of 82 queries was then reviewed with a larger group of experts and further refined. Additional classification parameters were added, for a total of 31 parameters and an ‘other’ category. Some classifications were subdivided into more granular classifications, such as treatment was subdivided into surgery/radiation and protocols/regimens.</p> <p>In classifying each query, the exact words from each query that fit the classification scheme were noted. For example, consider the query, “What environmental exposures are typically associated with the development of salivary gland cancer?”; this query is classified as an exposure (environmental exposure), a diagnosis or specific cancer type (salivary gland cancer), and a tumor location (salivary gland). </p> <p>For a central query to be effective, we felt that the use of preferred terms for each classification parameter would be useful, so each was mapped to a relevant terminology or ontology. For example, exposure data is represented in the Environmental and Exposures Ontology (ECTO), as well as NCIt. The Uber Anatomy Ontology (Uberon) contains classifications of anatomical structures, which can be used to classify tumor locations. Many of the parameters covered by specialized terminologies are also covered by the NCIt. In some cases, the parameters were covered by multiple ontologies. At some point a preferred terminology will need to be selected for each parameter, perhaps informed by an assessment of what is being used in the CRDC data. It is likely that all terminologies and ontologies will need to be extended to cover all the terminology needed. Mappings between these terminologies and those used in the CRDC data will need to be developed. </p> <p>Finally, categories were prioritized based on how relevant and feasible they were for the CRDC as either high priority, nice to have or low priority.</p>
DataQI.NET: A Framework for Specifying Query Criteria Using the Repository Pattern
<p>During the construction of applications that require integration with databases, developers often dedicate a considerable amount of time writing the data access layer. Although there are some tools that aim at improving that process, it still demands a great amount of (usually default) code, which is error-prone, takes effort and time from the developer, and reduces the code reuse potential. This paper presents the DataQI.NET, a framework that encapsulates the complexity of those tools and makes possible the definition of customized queries through method signatures on interfaces that follow the Repository Pattern, allowing the developer to focus more on the business rules implementation. A comparative study is provided considering the implementation with and without the framework, demonstrating that, by using DataQI.NET, we obtain more homogeneous and standardized query methods that communicate, supporting code reuse and significant reduction on the lines of code and implementation time.</p>
Sony AIBO robot dog accelerometer query
<p>Dataset taken from https://www.cs.unm.edu/~mueen/carpet_query.txt</p> <p>Stored on Zenodo as backup for Stumpy Fast Pattern Matching Tutorial https://stumpy.readthedocs.io/en/latest/Tutorial_Pattern_Searching.html .</p>
Querying variants. Boccaccio's Commedia
<p>6th Project Presentation</p>
ORE 2014 Live Competition Queries
<p>The queries of the ORE 2014 Live Competition (http://vsl2014.at/meetings/ORE-competition.html). The competition framework can be found at GitHub (https://github.com/andreas-steigmiller/ore-2014-competition-framework) and the ontologies and reasoners are also available at Zenodo (https://zenodo.org/record/10791 and https://zenodo.org/record/11145, respectively).</p>
ContentMine Analysis of Papers from EuPMC query zika
<p>Facts and metadata extracted from papers returned from querying EuropePMC with the phrase 'zika' using the ContentMine Cambridge pipeline. With dictionaries available at https://github.com/contentmine/dictionaries. Query and dictionaries used as of 2016-08-31</p>
Wikidata query for ICONGRAPH1.0
<p>This is the results of the query used to create "IICONGRAPHwikidata 1.0"</p>
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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.