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556 results for “meta data”
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </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>
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>
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>
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>
Data for the publication "Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer"
<p>This dataset encompasses all data needed to reproduce the analyses presented in <a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></p> <p>You can also check the <a href="https://github.com/zellerlab/crc_meta">GitHub repository</a></p>
RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)
<p>This is an RDFied version of the dataset published by Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a></p> <p>The Original publication authors: Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb</p>
Van Dijk et al. (2021), A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050, data and scripts
<p>This repository contains all data and R scripts to reproduce the figures in Van Dijk et al. (2021), A meta-analysis of global food demand and population at risk of hunger projections for the period 2010-2050, Nature Food. More specifically, it includes two databases: (1) A database with standardized information to describe the characteristics of 57 studies that were identified by the systematic literature review and (2) The Global Food Security Projections Database v1.0.1 with harmonized projections for three global food security indicators: food consumption in kcal per capita and total kcal, and population at risk of hunger. The database also includes projections for total global population that are required to derive the global food security indicators.</p> <p>The two scripts (nf_figures.r and nf_meta_regression.r) can be used to reproduce the figures and tables in the main paper and the supplementary information. Please start with the first script, which sources the second script. </p> <p>This is the first version of the Global Food Projections Database. We expect to update the data, including additional studies and variables in the future. For issues and suggestions, please contact michiel.vandijk@wur.nl.</p> <p> </p>
Data: An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction
<p><strong>Dataset supporting the manuscript "</strong>An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction<strong>" by the authors of this dataset.</strong></p> <p><strong>Where to start</strong></p> <p>This Zenodo repository contains both raw data and runnable code for the manuscript "An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction". The runnable code is best executed directly at CodeOcean (https://doi.org/10.24433/CO.6934377.v1). Alternatively, CodeOcean capsules are Docker images and can be run locally after download and unzipping. The full CodeOcean capsule is stored here as "CodeOceanCapsule_Injectable_meta_biomaterial.zip", it contains all the information and data to full reproduce the evaluation underpinning the manuscript " An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction".</p> <p>Quantitative raw data, in the form of text files, Excel files and R-data files useful for the data evaluation are included in "CodeOceanCapsule_Injectable_meta_biomaterial.zip". As especially the numerical simulation files are rather voluminous (100GB), we also provide a copy of the capsule without this large part, which however otherwise remains runnable for most evaluations ("CodeOceanCapsule_Injectable_meta_biomaterial_no_raw_simulation.zip"), and, for lightweight documentation of the code section only "CodeOceanCapsule_Injectable_meta_biomaterial_code_only.zip". The results of a capsule run are also provided, as "CodeOceanCapsule_Injectable_meta_biomaterial_results_run_4899036.zip".</p> <p>Besides archival of the CodeOcean evaluation capsule, this repository contains additional imaging data from which some of the quantitative data treated in the CodeOcean capsule was extracted, and additionally raw files for the illustrative figures in the manuscript. This data is contained in the files "Raw_images_For_Figure_1.zip", "Raw_images_For_Figure_3.zip", "Raw_images_For_Figure_4.zip"; "Raw_images_For_Figure_5.zip", "Raw_images_For_SFigure_S6.zip", "Raw_images_For_SFigure_S8.zip", "Raw_images_For_SFigure_S9.zip", "Raw_images_For_SFigure_S19.zip".</p> <p><strong>External dependencies</strong></p> <p>To facilitate centralized software development and installation, custom R and Python libraries used by the CodeOcean capsule "CodeOceanCapsule_Injectable_meta_biomaterial.zip" are hosted on Github, with releases archived in separate Zenodo repositories. These libraries are included automatically during the build phase of the CodeOcean capsule.</p> <p>This concerns the Python discrete particle simulation particleShear (DOI: <a href="https://doi.org/10.5281/zenodo.4589212">10.5281/zenodo.4589212</a>), and the R packages textureAnalyzerGels (for analysis of mechanical compression curves, DOI: <a href="https://doi.org/10.5281/zenodo.4589276">10.5281/zenodo.4589276</a>), rheologyEvaluation (for analysis of oscillatory sweep rheology, DOI: <a href="https://doi.org/10.5281/zenodo.4594353">10.5281/zenodo.4594353</a>), particleShearEvaluation (evaluation of the output of the Python simulations, DOI: <a href="https://doi.org/10.5281/zenodo.4594649">10.5281/zenodo.4594649</a>), plot.counts (convenience functions for scientific plotting, DOI: <a href="https://doi.org/10.5281/zenodo.4589498">10.5281/zenodo.4589498</a>) and reproducibleCalculationTools (numerical comparision of subsequent evaluations to validate reproducibility, DOI: <a href="https://doi.org/10.5281/zenodo.4594515">10.5281/zenodo.4594515</a>).</p> <p>For automated evaluation of ImageJ macros from Excel files, we also developed an Excel macro runner plugin in ImageJ, termed PoreSizeExcel (DOI: <a href="https://doi.org/10.5281/zenodo.4589546">10.5281/zenodo.4589546</a>). While the R and Python libraries listed above are actively loaded and used by the CodeOcean capsule, we used the PoreSizeExcel ImageJ plugin manually to streamline our quantitative image treatment, but not in a fully automated fashin.</p> <p>The Zenodo archives cited above reproducibly provide the state of the libraries as used for evaluation of this dataset, we continue to develop the libraries and continuously make them available at Github ( at <a href="https://github.com/tbgitoo">https://github.com/tbgitoo</a> ).</p> <p><strong>Version history</strong></p> <p>This is the third version of this Zenodo repository.</p> <p>We undertook major efforts from version v1.0 to the present version v2.0 to increase reprodubility of evaluation (via the use of the CodeOcean platform) and via separation of generic libraries (listed above, and installable on their own independently of this particular project) from specific project-associated data and evaluation (here). For this reason, while the data is maintained and in part completed due to new experiments having been carried out in the mean time, the structure of the repository has undergone major changes from v1.0 to the present version v2.0.</p> <p>With this version v3.0 we added raw data on cell transplantation, and completed the CodeOcean capsule, including adaptation to peer review changes to the manuscript.</p>
GDC-PANCAN.htseq_counts and associated meta-data no longer available from gdc.xenahubs.net
<p>The RNA-seq data and associated meta-data I used for my publications, downloaded from gdc.xenahubs.net but no longer available in this form (i.e. raw counts) from the website. </p>
Data from: Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis
<p>Data and analysis in R for the publication "Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis" by Ossanna & Gornish (2023), <em>Journal of Applied Ecology</em>, <em>60</em>(2), 218-228. <a href="http://doi.org/10.1111/1365-2664.14324">https://doi.org/10.1111/1365-2664.14324</a>.</p>
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </p>
Section 5.1 "Task Area 1: Image (meta)data formats and standardization)" Figure 8
<p>Figure 8. A FAIR-IO bundle combines the necessary acquisition and provenance metadata together with multi-resolution, chunked binary data in a single cloud-compatible format for simplified sharing and re-use.</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
Data from Time since liver transplantation and immunosuppression withdrawal outcomes: a systematic review with individual patient data meta-analysis
<p>This record provides one CSV file containing anonymized individual patient data (IPD) of pre-withdrawal times (in days) of liver transplant recipients that underwent immunosuppression (IS) withdrawal. Collection and publication of anonymized data was approved by the Ethics Committee Northwest and Central Switzerland. Patients of 15 primary studies are stratified by successfully reaching the state of IS-free operational tolerance (OT) or by developing signs of immunological rejection (non-OT).</p>
Data for study Conventional land-use intensification reduces species richness and increases production: A global meta-analysis
Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, this dataset was created and a global meta‐analysis on it was carried out, thus synthesizing 115 studies. The dataset consists of 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. It was found that across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. These findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results, which were published in a paper titled Conventional land‐use intensification reduces species richness and increa
Data from Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies
<p>This dataset contains three text files in RIS format. They represent the screening process during study selection for "Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies" (<a href="https://doi.org/10.1111/liv.14179">https://doi.org/10.1111/liv.14179</a>). The file DOKU_All TiAb-Screening_20200116_cap contains all 3453 records (merged from original and update search) that were subjected to title-abstract screening. The file DOKU_All FT-Screening_20200116_cap contains all 174 records that were subjected to full-text screening. The file DOKU_All Included_20200116_cap contains all 26 records that were included into the final review.</p> <p>In addition, a PRISMA flow diagram (Fig. 1 in the paper) is available in TIF format.</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.