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1,680 results for “Meta”
Materials for "Poor nutritional condition promotes high-risk behaviours: A systematic review and meta-analysis"
<p>This contains a permanent record of dataset and analysis code for the study:</p> <p>Moran, N. P., Sánchez‐Tójar, A., Schielzeth, H., & Reinhold, K. (2021). Poor nutritional condition promotes high‐risk behaviours: a systematic review and meta‐analysis. <em>Biological Reviews</em>, <em>96</em>(1), 269-288.</p> <p>Full data analysis records are available on https://osf.io/3tphj/</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 836937. Also, this research was funded by the German Research Foundation (DFG) as part of the SFB TRR 212 (NC³) – Project numbers 316099922 and 396782608.</p>
Data Set: fMRI Meta-Analyses
<p>This data set is the result of a systematic search in <strong>PubMed </strong>and <strong>APA PsycINFO</strong> for <strong>fMRI meta-analyses.</strong></p> <p>These records would be suitable for:</p> <ul> <li>meta-meta analysis on fMRI meta-analyses</li> <li>research questions regarding neuroimaging meta-analysis methodology (especially for those interested in coordination-based meta-analyses (CBMA): activation likelihood estimation (ALE) using GingerALE software, multi-level kernel density analysis (MKDA), seed-based d mapping (SDM) or image-based meta-analyses.</li> </ul> <p><strong>NOTE: These data are the raw search results from PubMed and PsycINFO and have been deduplicated but have NOT been screened for any inclusion criteria. This means you may find records in these results that are not, in fact, meta-analyses but still have the search terms (below) present in the title or abstract of the paper. </strong></p> <p>The data set is available in three formats: .csv, .ris, and a <a href="https://www.zotero.org/groups/4150721/fmri_meta-analyses">Zotero shared library</a></p> <p><strong>Search documentation</strong>: </p> <p>Search Date: May 21, 2021</p> <p>Conducted by: Meghan Testerman, Behavioral Sciences Librarian, Princeton University, mtesterman@princeton.edu</p> <p> </p> <p>PubMed: 433 records identified</p> <p>PubMed Search query (exact): (meta-analysis[Title]) AND (fMRI[Title/Abstract])</p> <p> </p> <p>PsycINFO: 289 records identified</p> <p>PsycINFO Search Query (exact): (TI meta-analysis) AND (TI fMRI OR AB fMRI)</p> <p><br> <strong>Total Results</strong></p> <p>Pubmed (433) + PsycINFO (289) = 722</p> <p>Deduplicates removed: 234</p> <p>Unique records: 488</p>
Accompanying dataset; 'Agroforestry enhances biological activity, diversity and soil-based ecosystem functions in mountain agroecosystems of Latin America: A meta-analysis.'
<p>The database created as part of the meta-analysis is designed to facilitate the comparison of biological activity, diversity (BIAD), and ecosystem functions (EFs) between agroforestry systems (AFS) and other land-use types. It incorporates data extracted from selected studies, each record comprising a mean value, sample size, and a variance measure to compute standard deviation. The database also categorizes data according to 22 explanatory variables, including geographical coordinates, climate classification, soil type, AFS classification, and more, to characterize the sites and management systems involved. This detailed classification enables a nuanced analysis of how different factors might influence the BIAD and EFs in the context of AFS. The database supports the meta-analysis by allowing for the estimation of effect sizes using response ratios, which compare the relative difference in BIAD and EFs between AFS and other land uses. Data extraction from primary studies was meticulous, employing both direct and indirect methods such as graph digitizing software, and missing data were supplemented using reliable sources or direct communication with the original study authors. The comprehensive nature of this database ensures that the analysis can account for a wide range of variables that may affect the outcomes of interest in the meta-analysis. </p><p>For an in-depth exploration of the study's findings and methodology, refer to the comprehensive meta-analysis available in Global Change Biology (2024), entitled "<i>Agroforestry Enhances Biological Activity, Diversity, and Soil-Based Ecosystem Functions in Mountain Agroecosystems of Latin America: A Meta-Analysis</i>."</p>
Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis
<p>We sought to identify the best biomarkers for the early diagnosis of LC, using a systematic review of seven databases. We identified 79 articles that focused on the identification and assessment of diagnostic biomarkers and then performed a meta-analysis. This work has been submitted for publication.</p>
A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients
<p><span>A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients</span></p>
Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis
<p>Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of:<br>1. information on the database<br>2. legend<br>3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information</p>
GWAS summary stats in "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."
<p>Summary stats of the genome-wide meta-analysis for dental caries and periodontal diseases in our study (population A and B).</p> <p>Article "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."</p> <p>https://doi.org/10.1186/s12903-024-04799-1<br><br></p>
OpenCitations Meta CSV dataset of all bibliographic metadata
<p>This dataset contains all the bibliographic metadata (in CSV format) included in OpenCitations Meta. In particular, each line of the CSV file defines a bibliographic resource, and includes the following information:</p> <ul> <li><strong>[field "id"]</strong> the IDs for the document described within the line;</li> <li><strong>[field "title"]</strong> the document's title;</li> <li><strong>[field "author"]</strong> the authors of the document;</li> <li><strong>[field "pub_date"]</strong> the date of publication;</li> <li><strong>[field "venue"]</strong> information about the venue, i.e. the bibliographical resource to which the document belongs;</li> <li><strong>[field "volume"]</strong> the volume sequence identifier (e.g. a number) to which the entity belongs;</li> <li><strong>[field "issue"]</strong> the issuesequence identifier (e.g. a number) to which the entity belongs;</li> <li><strong>[field "page"]</strong> the page range of the resource described in the row;</li> <li><strong>[field "type"]</strong> the type of resource described in the row;</li> <li><strong>[field "publisher"]</strong> the entity responsible for making the resource available;</li> <li><strong>[field "editor"] </strong>the editors of the document.</li> </ul> <p>This version of the dataset contains:</p> <ul> <li>114,621,237 bibliographic entities</li> <li>298,847,794 authors and 2,465,711 editors (counted by their roles, without disambiguating individual</li> <li>711,711 publication venues</li> <li>241,783 publishers</li> </ul> <p>The zipped dataset weighs 11 GB, while, when extracted, it weighs 46 GB on an ext4 filesystem.</p> <p>Additional information about OpenCitations Meta at <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
Clinical evidence for high-risk CE-marked medical devices for glucose management: a systematic review and meta-analysis
<p><strong><span>Aims: </span></strong><span>High-risk medical devices are increasingly used in diabetes management, but<span><span> there are no specific European recommendations on how they should be evaluated. Within</span></span> the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we conducted a systematic review and meta-analysis evaluating CE-marked high-risk devices for glucose management. </span></p> <p><strong><span>Materials and Methods: </span></strong><span>We<span> identified interventional and observational studies evaluating the </span>efficacy and safety of 8 automated insulin delivery (AID) systems, 2 implantable insulin pumps, and 3 implantable continuous glucose monitoring (CGM) devices.<span> </span></span><span>We meta-analysed randomized controlled trials (RCTs) comparing AID systems with other treatments.</span></p> <p><strong><span>Results:</span></strong><span> 99 studies published from 2009–2022 were included, comprising 83 on AID systems, 6 on insulin pumps, and 10 on CGM; 43% reported industry funding;30% were pre-market; 45% had a comparator group. 33% were RCTs, 25% non-randomized trials, and 41% observational studies. Median sample size was 52 (interquartile range 25–111), age 37.8 years (17–45.5), and study duration 13 weeks (4.5–26). AID systems lowered HbA1c by 0.3 percentage points (absolute mean difference [MD]=-0.3; 9 RCTs; I<sup>2</sup>=85%) and increased time in target range for sensor glucose level by 10.5 percentage points (MD=10.5; 14 RCTs; I<sup>2</sup>=89%). 69% of studies reported on at least one safety outcome.</span></p> <p><strong><span>Conclusions:</span></strong><span> High-risk devices for glucose monitoring or insulin dosing, in particular AID systems, improve glucose control safely but evidence on diabetes-related end organ damage is lacking due to short study durations. Methodological heterogeneity highlights the need for d<span><span>eveloping standards for future pre- and post-market investigations of diabetes-specific high-risk medical devices.</span></span></span></p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'
<p>The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD.</p> <p>The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results.</p> <p>This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’. </p> <p>In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy.</p> <p>References:</p> <p>Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>6</em>, 1–24. https://doi.org/10.1177/23969415211010423</p> <p>Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>5</em>, 1–24. https://doi.org/10.1177/2396941520946</p> <p> </p>
Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis
<p><span><span> </span></span><span>The file named <span>“</span>Rawdata.xlsx<span>”</span> contains data sourced from the literature.<span> The file name is “GE_β.tif<span>”</span><span>,</span></span></span><span><span> GE represents</span></span><span> global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.</span></p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
Summary Statistics from "Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2"
<p>GWAMA summary statistics of PCSK9 levels using fixed-effect model. Genome-wide data is given for Europeans with statin adjustment and Europeans without statin treatment only (subset of the population). In addition, locus-wide data of the PCSK9 gene locus for African-Americans without statin treatment is listed.</p> <p>When using this data, please cite: Pott J, Gadin J, Theusch E, et al.. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Hum Mol Genet. 2021 Sep 30:ddab279. doi: 10.1093/hmg/ddab279. PMID: 34590679</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
Variation in Detected Adverse Events using Trigger Tools: A Systematic Review and Meta-Analysis
<p>Raw data sets for the meta-analysis.</p> <p>Data collection file with all the information extracted from the included studies.</p> <p>QAT file with the information from the quality assessment tool (QAT) for all included studies.</p> <p>ReadMe with information on data sets and updates.</p> <p>Codebooks for both data sets.</p>
METAS VNA Tools project of a niobium CPW in different conduction states at 4 Kelvin, 10 Kelvin and at 25 Kelvin
<p>A METAS VNA Tools project which contains S-parameter data of a plain CPW line on a 1cmx1cm silicon chip made from intrinsic silicon. The chip is operated at 4 Kelvin, 10 Kelvin and 25 Kelvin with currents up to 200 mA for further heating injected. The temperature, current and DC resistance are noted in each filename.</p>
Carbon sequestration in riparian forests: a global meta-analysis data set
<p>Data collected for a global meta-analysis of riparian forest biomass and soil carbon stocks. Includes studies estimating the carbon stored in the soil or standing live and dead woody vegetation, or the total biomass of woody vegetation in plots described as "riparian" or "floodplain". Also includes soil carbon metrics for plots considered to be "baseline" plots paired with a riparian plot. Excludes studies focused solely on depressional or tidal wetlands, plots lacking woody vegetation, greenhouse experiments, or those that measured only the biomass or carbon content of individual plants.</p> <p>The data file includes DOIs for all studies included (where available), study area coordinates, descriptions of study plots, vegetation age and soil texture (if known), reported values for woody biomass, biomass carbon stock, soil bulk density, soil carbon concentration, soil carbon stock, and/or soil sampling depth. All field descriptions are provided in the accompanying metadata file.</p>
Data archive for Pepper, Bateson and Nettle, 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis'
<p>Data archive for the paper 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis' by Gillian Pepper, Melissa Bateson and Daniel Nettle. This version was uploaded in July 2018 after peer-review in the journal Royal Society Open Science. Compared to earlier version, it incorporates some minor error correction to the dataset, and reflects the revised analyses we performed after peer review. </p> <p>Our protocol and recording guide, which were preregistered on the Open Science Framework in 2016, are also included here, as is our PRISMA diagram.</p> <p>The data file 'unprocessed data' contains the data as extracted from the literature, with associations shown both as provided in the original papers, and converted to correlation coefficients. The algorithms for converting all the different associations to correlation coefficients are described in the flowchart and implemented in the R script 'effect conversion algorithms.r'.</p> <p>The data file 'processed data.csv' is the dataset analysed in the paper. Compared to 'unprocessed data.csv', it excludes: associations from studies of non-human animals; duplicate associations; a small number of associations from studies of medical treatments; and associations considered subparts or subscales of other associations. These exclusions are outlined in Methods section of the paper. In addition, in the processed data file, all correlations are aligned in direction so as to make them comparable (variable 'ValencedEffect'); and all associations are assigned to broad and fine categories.The script 'unprocessed to processed.r' makes the processed data file from the unprocessed one, or you can simply work from the processed one directly. </p> <p>The R script 'telomere metanalysis script RSOS REVISED.r' reproduces the analyses found in the paper.</p> <p>This version of the archive (July 17 2018) contains one small correction in the data files compared to all earlier versions. </p>
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
EukZoo, an aquatic protistan protein database for meta-omics studies.
<p>This database contain protein sequences of aquatic microbial eukaryotes, or protists. The purpose of this is to make a database that is of reasonable quality to serve as resource for both taxonomy and functional interpretation of metagenomic and metatranscriptomic studies of protists. The source of the sequences were mainly from Marine Microbial Eukaryotes Transcriptome Sequencing Project (MMETSP), and supplemented with various genomes and transcriptomes of organisms that were not a part of MMETSP.</p> <p>To use this database, one has to understand the main function of the three files here.</p> <p>(1) The protein sequences are stored in .faa file. You can build an alignment/search database out of that and search your meta-omics sequences against it. Each sequence in the FASTA file has an ID which always consists of two parts like this: "MMETSP0004_1234567". The text before the first underscore is the source ID of that sequence.</p> <p>(2) Taxonomy information of each source ID are stored in "EukZoo_taxonomy_table_v_0.2.tsv". One can use the information within in conjunction with database search results to assign taxonomy to sequences.</p> <p>(3) KEGG annotation of each sequence are stored in "EukZoo_KEGG_annotation_v_0.2.tsv". One can use the information within in conjunction with database search results to assign KEGG functional annotation (KO ID) to sequences.</p> <p>I also provide scripts to assign taxonomy and KEGG annotation from database search results. You can also find the scripts and explanations on how to use them on the <a href="https://github.com/zxl124/EukZoo-database">EukZoo GitHub page</a>. You will find details on how the database was created and curated on there as well.</p> <p>Please contact me at zhenfeng.liu1@gmail.com if you have any questions or requests. Thank you for your interest in EukZoo.</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.