Four clinical centers participated in this 11 year, prospective observational study of knee osteoarthritis. The goal of the research was to identify biochemical, genetic, and imaging biomarkers associated with the development and progression of osteoarthritis. Evaluation of the cumulative data in comparison with structural and/or clinical outcomes is expected to provide insights into the prevention and treatment of the disease. Over the course of the study, data were collected from 4,796 subjects from over 431,000 clinical and imaging visits resulting in close to 26,626,000 images in the archive. Multiple datasets from this research include: Participant Information (demographic & cohort, measures inventory, outcomes); AllClinical Dataset (multiple datasets with subject risk factors, joint symptom/function, medical history, physical exam, nutrition, & biomarkers (summary of phlebotomy and urine specimen collection times)); Medication Inventory; Knee MR Image Assessments (quantitative cartilage morphometry, semi-quantitative scoring); Knee MRI Metaanalysis; Knee X-Ray Image Assessments; Knee X-Ray Metaanalysis; FNIH Project (post-processed OAI image data as well as serum and urine evaluations from a subset of one of the cohorts); Ancillary Studies (accelerometry measurements, Bone Quality MRI and DEXA measurements, Pivotal OAI MRI Analyses (POMA), and Skin Auto-Fluorescence (Sage) measurements).

Prognostic model summary information of the Osteoarthritis Initiative (OAI) and Multicentre Osteoarthritis Study (MOST) datasets.

Values are mean±standard deviation or number of patients with percentage in parentheses; OAI: osteoarthritis initiative; () Kruskal-Wallis non-parametric test for comparison between mtDNA haplogroups; (#) Chi-square test; BMI: body mass index; KL: Kellgren and Lawrence; mJSN: Joint space narrowing in medial compartment; (*) The worst knee and the less severe knee at baseline were designated, respectively, as the knee with the highest and lowest KL (Kellgren andLawrence) grade, OARSI JSN (joint space narrowing) grade, OARSI osteophytes grade or OARSI sclerosis grade, as appropriate on each case; significant p-values are in bold.Demographic characteristics of the study population at baseline grouped by mitochondrial DNA (mtDNA) haplogroups in the progression subcohort of the OAI.

Comparison of NHANES-III and OAI study characteristics.

The purpose of this study was to explore patterns of baseline physical activity using Multilevel Functional Principal Components Analysis (MFPCA) and examine the relation of these patterns with incident slow gait speed over 4 years in adults with or at high risk of knee OA utilizing data from the accelerometer sub-study of the Osteoarthritis Initiative (OAI). For the purpose of the study, Multilevel Functional Principal Components Analysis was done. As a result of the analysis, it was determined that there were 4 activity patterns (PCs), 1) high activity, 2) high evening activity, 3) high morning and evening activity, and 4) very high morning activity. Those whose daily activity patterns best matched with the pattern PC2 or PC4, were found to have 0.60 and 0.39 times the risk of developing slow gait speed, respectively; while those whose daily activity patterns least matched PC3, were found to have 1.77 times the risk. Our study results suggest that daily activity patterns may be associated with the development of slow gait speed in adults with or at risk for knee OA.

OSTI has established an OAI (Open Archives Initiative) server to allow harvesting of metadata for full-text DOE research and development reports contained in OSTI.GOV. Included are reports in phsyics, chemistry, materials, biology, environmental sciences, energy technologies, engineering, computer and information science, renewable energy, and other topics. These reports are produced by DOE, the DOE National Laboratories, and DOE contractors and grantees primarily from 1991 forward.

Percentages are weighted using the NHANES-III sampling weights.Percentage of persons in fair/poor health was not estimated because there were zero persons with a missing race and missing knee pain in NHANES-III.**Percentage of persons in fair/poor health was not estimated because there was only one person with a missing obesity status and missing knee pain status in OAI.

A dataset of metadata for UK academic institutional repositories, including a census of research software contained.

    URL
    The OAI url


    id
    CORE Identifier


    openDoarId
    Open DOAR identifier


    name
    Name of repository


    Russell_member
    If the university is a member of the Russell Group of research intensive universities


    RSE_group
    If an RSE group is present (based on Soc of RSE data)


    email
    Redacted


    uri
    Not used


    uni_sld
    Second level domain (the part of the url between . And .ac.uk


    homepageUrl
    University website


    source
    Not used


    ris_software
    the Research Information System software used


    ris_software_enum
    Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)


    metadataFormat
    the protocol used for metadata


    createdDate
    Repository creation date


    location
    location of university


    logo
    University logo (resolves in error)


    type
    Only = Repository for this dataset. Can be = journal etc.


    stats
    Not used


    contains_software_set
    Whether the OAI-PMH software set is present in the repository.


    Num_sw_records
    The response of the OAI-PMH query for software (erroneous as discussed in paper)


    Error
    The category of error returned by the experiment’s OAI-PMH queries (see paper)


    Manual_Num_sw_records
    The true amount of software contained in the repository as found by a manual exhaustive search of each university website


    Category
    Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar

Data used to understand knee osteoarthritis (KOA) often involves knee-level, rather than person-level information. Failure to account for the correlation between joints within a person may lead to inaccurate inferences. The aim of this study was to develop a flexible, data-driven framework for predicting knee pain outcomes, incorporating the advantages of both random forest (RF) and mixed effects models for correlated data. Specifically, we utilized data from the baseline visit of the Osteoarthritis Initiative (OAI) and applied the Binary Mixed Models (BiMM) algorithm to predict two binary dependent variables. 1) presence of knee pain, stiffness or aching in the past 12 months and 2) presence of knee pain indicated by a KOOS pain score > 85. This novel approach was compared to standard random forests (RF), which do not account for correlations among knees. This study demonstrates the potential of BiMM as a predictive tool for KOA pain, achieving a comparable or slightly improved performance over traditional RF models while simultaneously accounting for within-person correlation among knees. This is a significant advancement, as most machine learning models to date have only considered each knee individually. These findings support the integration of BiMM in KOA outcome prediction, providing a nuanced alternative to existing models and advancing our understanding of important KOA outcomes on the person level. Although demonstrated here for KOA, this method is relevant to any situation where within-person correlations are relevant, including other joints and other musculoskeletal conditions.

Replication code for extracting and analysing data access categories from the oai-pmh feed provided by the GESIS - Leibniz Institute for the Social Sciences DBK data catalogue. The code utilises the dc_oai-de feed to extract metadata about objects in the data catalogue, this is then edited to retain and summarise information on the four data access categories used by the archive. The oai-pmh metadata is available from GESIS under a CC0 licence.

The .csv files extracted from the oai-pmh feed and edited to correct for missing records is also included for replication.

The dataset provides the data extracted for the scoping review of the literature on the study designs for clinical trials applied to personalised medicine, as part of the EU project on “Personalised Medicine Trials” (PERMIT). The dataset reports the references for all articles selected as part of the scoping review, as well as information on the general study characteristics and definition, methodology, statistical considerations, and examples of each study design referred to in each included paper.

English syntax of the study: Pain coping and health care use in patients with early knee and/or hip osteoarthritis: 10-year follow-up data from the Cohort Hip and Cohort Knee (CHECK) study.

The following dataset was generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS".

Project funded by Fed4FIRE+ OC5 (https://www.fed4fire.eu) under grant 732638.

The dataset provides network metrics captures across several days employing a GStreamer-based MPEG-DASH player running on an UE connected to a LTE network.

Nitos LTE/OpenAirInterface (OAI) testbed (https://nitlab.inf.uth.gr/NITlab/nitos/lte) was used to deploy the LTE network.

CDN-like server/DASH Dataset -> Internet -> EPC/OAI -> eNodeB/OAI -> UE/DASH player

The player downloads MPEG-DASH video files provided by Distributed DASH dataset (https://dash.itec.aau.at/distributed-dash-datset/), a dataset for CDN-like experiments, and captures the following data:

Date: date when the data is collected

Player: type of the player (in this case it is always "GStreamer")

Num: identifier of the player

URLVid: URL of the MPD file

Latency: latency experienced by the player

BW: bandwidth experienced by the player

Quality: chosen DASH video representation

During the experiments, other players run in order to generate realistic media streaming traffic at the CDN-like servers. These players start playing by following Poisson or Pareto distribution.

The dataset was used to train Machine Learning Time Series predictor in order to forecast network capabilities and can be used for further experimentation concerning time series analysis.

The study focuses on the opinion of people about alcohol and drugs use, and the alcohol and drugs use of the respondent personally.

The Guidelines specify the interoperability layer between Current Research Information Systems (CRIS) and the OpenAIRE infrastructure. The information interchange is based on the Common European Research Information Format (CERIF) data model, the CERIF XML exchange format, and the OAI-PMH protocol. The Guidelines are intended mainly for implementers and administrators of CRIS who plan to communicate research information to OpenAIRE. OpenAIRE (openaire.eu) is the European infrastructure enabling researchers to comply with the European Union requirements for Open Access to research results. OpenAIRE collects metadata from a variety of data sources: publication repositories, data archives and CRIS across Europe and beyond. Interoperability guidelines are defined for each type of source. CERIF is a standard data model for research information and a recommendation by the European Union to its Member States. The custody of CERIF has been entrusted by the European Union to euroCRIS (eurocris.org), an international not-for-profit organisation dedicated to the interoperability of CRIS.

This fileset contains a preprint version of the conference paper (.pdf), presentation slides (as .pptx) and the dataset(s) and validation schema(s) for the IDCC 2019 (Melbourne) conference paper: The Red Queen in the Repository: metadata quality in an ever-changing environment. Datasets and schemas are in .xml, .xsd , Excel (.xlsx) and .csv (two files representing two different sheets in the .xslx -file). The validationSchemas.zip holds the additional validation schemas (.xsd), that were not found in the schemaLocations of the metadata xml-files to be validated. The schemas must all be placed in the same folder, and are to be used for validating the Dataverse dcterms records (with metadataDCT.xsd) and the Zenodo oai_datacite feeds respectively (schema.datacite.org_oai_oai-1.0_oai.xsd). In the latter case, a simpler way of doing it might be to replace the incorrect URL "http://schema.datacite.org/oai/oai-1.0/ oai_datacite.xsd" in the schemaLocation of these xml-files by the CORRECT: schemaLocation="http://schema.datacite.org/oai/oai-1.0/ http://schema.datacite.org/oai/oai-1.0/oai.xsd" as has been done already in the sample files here. The sample file folders testDVNcoll.zip (Dataverse), testFigColl.zip (Figshare) and testZenColl.zip (Zenodo) contain all the metadata files tested and validated that are registered in the spreadsheet with objectIDs.
In the case of Zenodo, one original file feed,
zen2018oai_datacite3orig-https%20_zenodo.org_oai2d%20verb=ListRecords%26metadata
Prefix=oai_datacite%26from=2018-11-29%26until=2018-11-30.xml ,
is also supplied to show what was necessary to change in order to perform validation as indicated in the paper.

For Dataverse, a corrected version of a file,
dvn2014ddi-27595Corr_https%20_dataverse.harvard.edu_api_datasets_export%20
exporter=ddi%26persistentId=doi%253A10.7910_DVN_27595Corr.xml ,
is also supplied in order to show the changes it would take to make the file validate without error.

About Dataset

Dataset name: arXiv academic paper metadata Data source: https://arxiv.org/ Submission date: 1986-04-25 ~ 2025-05-13 (data updated weekly) Number of papers: 2,710,806 (as of 2025.5.14) Fields included: title, author, abstract, journal information, DOI, etc. Data format: json Data volume: 4.58G

  About ArXiv

For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of… See the full description on the dataset page: https://huggingface.co/datasets/jackkuo/arXiv-metadata-oai-snapshot.

Comprehensive dataset of 1 Public swimming pools in Thanh Oai, Hanoi, Vietnam as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset accompanies a study designed to test the temporal model hypothesis for the mechanism and treatment of central sensitization. This study uses a cohort retrospective multivariate analysis using a modified adaptive platform design. The analysis is done using the Halili physical therapy statistical analysis tool HPTSAT. The dataset includes raw table and expended results

Characteristics of included individuals from the OAI baseline cohort (n = 2859).