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17 results for “knowledge discovery”
Data from: A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics
<p>Data and results from the Imageomics Workflow. These include data files from the Fish-AIR repository (https://fishair.org/) for purposes of reproducibility and outputs from the application-specific imageomics workflow contained in the Minnow_Segmented_Traits repository (https://github.com/hdr-bgnn/Minnow_Segmented_Traits).</p> <p>Fish-AIR:<br> This is the dataset downloaded from Fish-AIR, filtering for Cyprinidae and the Great Lakes Invasive Network (GLIN) from the Illinois Natural History Survey (INHS) dataset. These files contain information about fish images, fish image quality, and path for downloading the images. The data download ARK ID is dtspz368c00q. (2023-04-05). The following files are unaltered from the Fish-AIR download. We use the following files:</p> <p>extendedImageMetadata.csv: A CSV file containing information about each image file. It has the following columns: ARKID, fileNameAsDelivered, format, createDate, metadataDate, size, width, height, license, publisher, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>imageQualityMetadata.csv: A CSV file containing information about the quality of each image. It has the following columns: ARKID, license, publisher, ownerInstitutionCode, createDate, metadataDate, specimenQuantity, containsScaleBar, containsLabel, accessionNumberValidity, containsBarcode, containsColorBar, nonSpecimenObjects, partsOverlapping, specimenAngle, specimenView, specimenCurved, partsMissing, allPartsVisible, partsFolded, brightness, <br> uniformBackground, onFocus, colorIssue, quality, resourceCreationTechnique. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>multimedia.csv: A CSV file containing information about image downloads. It has the following columns: ARKID, parentARKID, accessURI, createDate, modifyDate, fileNameAsDelivered, format, scientificName, genus, family, batchARKID, batchName, license, source, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>meta.xml: A XML file with the metadata about the column indices and URIs for each file contained in the original downloaded zip file. This file is used in the fish-air.R script to extract the indices for column headers.</p> <p>The outputs from the Minnow_Segmented_Traits workflow are:</p> <p>sampling.df.seg.csv: Table with tallies of the sampling of image data per species during the data cleaning and data analysis. This is used in Table S1 in Balk et al. </p> <p>presence.absence.matrix.csv: The Presence-Absence matrix from segmentation, not cleaned. This is the result of the combined outputs from the presence.json files created by the rule “create_morphological_analysis”. The cleaned version of this matrix is shown as Table S3 in Balk et al.</p> <p>heatmap.avg.blob.png and heatmap.sd.blob.png: Heatmaps of average area of biggest blob per trait (heatmap.avg.blob.png) and standard deviation of area of biggest blob per trait (heatmap.sd.blob.png). These images are also in Figure S3 of Balk et al.</p> <p>minnow.filtered.from.iqm.csv: Filtered fish image data set after filtering (see methods in Balk et al. for filter categories).</p> <p>burress.minnow.sp.filtered.from.iqm.csv: Fish image data set after filtering and selecting species from Burress et al. 2017.</p>
Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data
<p>This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data.<br> <br> ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.<br> <br> Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)</p>
Data associated with "A collaborative filtering based approach to biomedical knowledge discovery"
<p>This is the data set associated with the publication: "A collaborative filtering based approach to biomedical knowledge discovery" published in Bioinformatics.</p> <p>The data are sets of cooccurrences of biomedical terms extracted from published abstracts and full text articles. The cooccurrences are then represented in sparse matrix form. There are three different splits of this data denoted by the prefix number on the files.</p> <p>1. All - All cooccurrences combined in a single file</p> <p>2. Training/Validation - All cooccurrences in publications before 2010 in training, all novel cooccurrences in publication in 2010 go in validation</p> <p>3. Training+Validation/Test - All cooccurrences in publication upto and including 2010 in training+validation. All novel cooccurrences after 2010 in year by year increments and also all combined together</p> <p> </p> <p>Furthermore there are subset files which are used in some experiments to deal with the computational cost of evaluating the full set. The associated cuids.txt file containing a link between the row/column in the matrix with the UMLS Metathesaurus CUIDs. Hence the first row of cuids.txt matches up to the 0th row/column in the matrix. Note that the matrix is square and symmetric. This work was done with UMLS Metathesaurus 2016AB.</p>
Knowledge Discovery from Porous Organic Cages Literature Using a Large Language Model
<p>This article presents a GPT-4-based literature reading method that incorporates multi-label text classification and a follow-up information extraction, in which the potential of GPT-4 can be fully exploited to rapidly extract valid information from the literature. In the process of multi-label text classification, the prompt-engineered GPT-4 demonstrated the ability to label text with proper recall rates according to the type of information contained in text, including authors, affiliations, synthetic procedures, surface area, and the CCDC number of corresponding cages. Additionally, GPT-4 demonstrated proficiency in information extraction, effectively transforming labeled text into concise tabulated data. Furthermore, we built a chatbot based on this database, allowing for quick and comprehensive searching across the entire database and responding for cage-related questions.</p>
Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery - Dataset
<h1>Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery - Dataset</h1> <h2> </h2> <h2>Dataset corresponding to <a href="https://doi.org/10.1145/3672608.3707798">https://doi.org/10.1145/3672608.3707798</a></h2> <div>This is the dataset corresponding to <a href="https://doi.org/10.1145/3672608.3707798">https://doi.org/10.1145/3672608.3707798</a> "Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery" with the goal of providing a systematic method to discover state-of-the-art security knowledge, focused on threats, measures, and properties from science and project literature with minimal manual effort, and providing software engineers with the awareness and means they need to apply the knowledge to their projects.</div> <p> </p> <h2>Structure of the files</h2> <div>The dataset contains the extraction prompt, the extraction results, and metadata.</div> <div>- <strong>ACM_IEEE_EU_Results.zip</strong> is an archive file with the metadata and extraction results with the following folder structure: </div> <div> - <strong>Meta/ </strong>contains the DOI, title, publication date, and pages (omitted for IEEE since it may contain intellectual property).</div> <div> - <strong>Extract/</strong> contains extraction results generated by the LLM in response to the extraction_prompt.txt file.</div> <div> - <strong>ExtractMeta/</strong> contains the metadata for the extraction, such as execution time.</div> <div>- <strong>extraction_prompt.txt</strong> is the prompt used to extract information from the science and project publications.</div>
Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
<p>This contains data described in detail in our paper, "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery", where we develop a novel algorithm called RPath that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures.</p>
Ensembles of knowledge graph embedding models improve predictions for drug discovery
<p>This contains data described in detail in our paper, "Ensembles of knowledge graph embedding models improve predictions for drug discovery". The metadata involves the different trained models that were used for prediction analysis as well as all the predictions from the trained models.</p>
Supplement tables of Facilitating Antiviral Drug Discovery by Genetic and Evolutionary Knowledge
<p>Table S1: 36 human targets for approved antiviral drugs downloaded from Drugbank,</p> <p>Table S2: Supplementary information for Table 2,</p> <p>Table S3: Pockdrug druggability prediction results.</p>
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)
<p>Dataset accompanying the paper "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)".</p>
International Journal of Hidden Data Mining and Scientific Knowledge Discovery
<p>Welcome to the first issue of the International Journal of Hidden Data Mining and Scientific Knowledge Discovery: http://scholarpage.org/journal</p> <p>We decided to use the following acronym HDSKD for the journal. Hidden data represents data or information that is more or less hidden, unseen or encrypted so that it becomes difficult to extract it. Scientific knowledge represents every information that comes by a scientific way, such as theories, experimental observations or scientific evidences of some phenomenon. The last term in the journal title, namely "Discovery" has a great importance, since it means that we are looking for scientific information particularly and hidden data in general.<br /> Authors are encouraged to submit their latest research results to the HDSKD journal Hence, we are proud to announce the opening of our first journal issue in hidden data mining and scientific knowledge discovery (a peer reviewed journal) and encourage authors to submit their latest research results to HDSKD.<br /> The criteria of selection are based on the review decision, which will lead to the acceptance or reject of the paper.<br /> The readers’ comments are also welcome and everyone is invited to read or download the papers for a scientific or academic purposes. The journal is a non-profit academic source of knowledge and does not permit any commercial use of the articles.</p> <p><br /> The Editorial Board</p>
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Deep learning and knowledge graph powered drug combination discovery against infectious diseases
<p>The Datasets and source codes for paper "<strong>Deep learning and knowledge graph powered drug combination discovery against infectious diseases</strong>".</p>
Bottom-up anytime discovery of generalised multimodal graph patterns for knowledge graphs
<p>These resources were generated during the research performed for a research paper with title "Bottom-up anytime discovery of generalised multimodal graph patterns for knowledge graphs", submitted in 2024. The research was performed by Xander Wilcke (first author), Rick Mourits, Auke Rijpma, and Richard Zijdeman, and was funded by the NWO CLARIAH-PLUS.</p> <p>File list</p> <p>This is the dataset that was used as input to our experiments. It contains the civil records of 100,000 Dutch citizen in RDF format (gzipped N-Triples), and was randomly sampled from the nationwide civil registry. All individuals in this dataset have been deceased for at least 100 years.</p> <p> - dataset_sample-100000.nt.gz</p> <p>This data was generated during the experiments. In other words, these are the (unfiltered) patterns that were founds during our research, and which are thus present in the 100,000 people sample that was used as input. The patterns are encoded as RDF (N-Triples) with provenance and metadata. </p> <p> - socchist-100K_output.tar.xz</p> <p>This file contains the results from the survey (cleaned) held during the evaluation of our research, and lists the Likert scores and free-form responses from the participants of the survey. The responses have been anonymised.</p> <p> - survey_results_240713_cleaned.csv</p>
Accelerated knowledge discovery from omics data by optimal experimental design
GEO Series GSE144604. Escherichia coli. 135 samples. Type: Expression profiling by high throughput sequencing.
HER2 data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains HER2 training and test sets used for evaluating MSclassifier and other packages in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The training set is comprised of 100 instances and 74 attributes while the test set is comprised of 62 instances and 74 attributes. The training samples were used to obtain results from a 10-fold cross-validation testing of how MSclassifier and other packages accurately predicted HER2-receptor status phenotypes in breast cancer in the article. The data used in the software article was obtained from the supplementary data of "Adabor ES, Acquaah-Mensah GK, Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer, Briefings in Bioinformatics 2019; 20 (2): 504–514, https://doi.org/10.1093/bib/bbx138" by permission of Oxford University Press. Here, it is reproduced by permission of Oxford University Press.</p> <p> </p>
A KNOWLEDGE DISCOVERY STRATEGY FOR RELATING SEA SURFACE TEMPERATURES TO FREQUENCIES OF TROPICAL STORMS AND GENERATING PREDICTIONS OF HURRICANES UNDER 21ST-CENTURY GLOBAL WARMING SCENARIOS
A KNOWLEDGE DISCOVERY STRATEGY FOR RELATING SEA SURFACE TEMPERATURES TO FREQUENCIES OF TROPICAL STORMS AND GENERATING PREDICTIONS OF HURRICANES UNDER 21ST-CENTURY GLOBAL WARMING SCENARIOS CAITLIN RACE*, MICHAEL STEINBACH*, AUROOP GANGULY**, FRED SEMAZZI***, AND VIPIN KUMAR* Abstract. The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurricanes or tropical cyclones are among the least understood in climate science but among the most fiercely debated in the context of adaptation decisions or mitigation policies. Here we show that a knowledge discovery strategy, which leverages observations and climate model simulations, offers the promise of developing credible projections of tropical cyclones based on sea surface temperatures (SST) in a warming environment. While this study motivates the development of new methodologies in statistics and data mining, the ability to solve challenging climate science problems with innovative combinations of traditional and state-of-the-art methods is demonstrated. Here we develop new insights, albeit in a proof-of-concept sense, on the relationship between sea surface temperatures and hurricane frequencies, and generate the most likely projections with uncertainty bounds for storm counts in the 21st-century warming environment based in turn on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Our preliminary insights point to the benefits that can be achieved for climate science and impacts analysis, as well as adaptation and mitigation policies, by a solution strategy that remains tailored to the climate domain and complements physics-based climate model simulations with a combination of existing and new computational and data science approaches.
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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.