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25 results for “pmc”
Polar mesospheric clouds from the Balloon Lidar Experiment (BOLIDE) during the PMC Turbo balloon mission
<p>This dataset contains BOLIDE lidar data obtained during the PMC Turbo balloon mission that was launched on 7 July 2018 from Esrange, Sweden and landed in Nunavut, Canada on 14 July 2018. The mission was designed to study small-scale atmospheric dynamics induced by breaking atmospheric gravity waves within the polar mesospheric cloud layer at ~82 km altitude. PMC Turbo floated at around 40 km altitude and carried seven digital cameras to image the polar mesospheric cloud layer and the first Rayleigh lidar to successfully operate from a balloon.</p> <p>The lidar data consists of volume backscatter coefficients of polar mesospheric clouds, available at 20 m vertical and 10 s temporal resolution, contained in a compressed netcdf file. The magnitude of volume backscatter coefficients scales with<br> the brightness of clouds imaged by the PMC Turbo cameras. The netcdf file further includes floating altitude, rotator angle (azimuth) as well as latitude and longitude of the lidar beam at 82 km altitude.</p> <p>Users are encouraged to contact us for discussion when using BOLIDE data.</p> <p>Data contact: natalie.kaifler@dlr.de</p> <p>References:</p> <p>PMC Turbo camera videos: https://svs.gsfc.nasa.gov/13073</p> <p>NASA Space Physics Data Facility: https://cdaweb.gsfc.nasa.gov/index.html/, select PMC Turbo</p> <p>DLR Institute mission database: https://halo-db.pa.op.dlr.de/mission/112</p> <p>Kaifler, N., Kaifler, B., Rapp, M., Fritts, D.C. The polar mesospheric cloud dataset of the Balloon Lidar Experiment BOLIDE. Earth System Science Data. In preparation.</p> <p>Kaifler, B., Rempel, D., Roßi, P., Büdenbender, C., Kaifler, N., and Baturkin, V.: A technical<br> description of the Balloon Lidar Experiment (BOLIDE), Atmos. Meas. Tech., 13, 5681–5695,<br> https://doi.org/10.5194/amt-13-5681-2020, 2020.</p> <p>Fritts, D. C., Miller, A. D., Kjellstrand, C. B., Geach, C., Williams, B. P., Kaifler, B.,<br> et al. (2019). PMC Turbo: Studying gravity wave and instability dynamics in the summer mesosphere<br> using polar mesospheric cloud imaging and profiling from a stratospheric balloon. Journal of<br> Geophysical Research: Atmospheres, 124, 6423– 6443. https://doi.org/10.1029/2019JD030298</p>
A dataset used to determine a semantic similarity metric based on UMLS for PMC-OA
<p>We have performed a series of in-silico experiments in order to determine a semantic similarity metric based on UMLS annotations for PubMed Central Open Access. Here we have stored the data used for and obtained from such experiments. We have worked with relevant and partially relevant articles from the TREC-2005 Genomics Track Collection, from now referred as the initial collection, including a total of 4240 unique PubMed articles. From those 4240 articles, only 62 had publicly available; those 62 articles correspond to the full-text collection.</p> <p>Our data comprises flat files using tabs as separators and one Excel sheet. Tab separated values always include a first row with headings:</p> <ul> <li>Stems extracted from title and abstract for articles in the initial collection. Each row contains a stem with its inverse-document-frequency (IDF) within the initial collection. Stems were calculated following the Porter algorithm (available at http://tartarus.org/martin/PorterStemmer/java.txt) <ul> <li>stems.TA.tsv</li> </ul> </li> <li>Article profiles, i.e., terms (either word stems or UMLS concepts) found in the articles with term frequency (TF) and IDF. The first two columns correspond to PubMed Identifier (PMID) and PubMed Central identifier (PMC). PMC identifier was set to 0 whenever full-text was not available. <ul> <li>profiles.TA.tsv: Profiles according word stems in title and abstract for the initial collection</li> <li>profiles.PMID.tsv: Profiles according to UMLS concpets in title and abstract for the initial collection</li> <li>profiles.PMC_TA.tsv: Profiles according to UMLS concepts in title and abstract for the full-text collection</li> <li>profiles.PMC.tsv: Profiles according to UMLS concepts in the full-text for the full-text collection</li> </ul> </li> <li>Similarity matrixes calculated on the article profiles with PubMed Related Article metric (PMRA), BM25, and Cosine. There are matrixes for terms found in title-and-abstract as well as full-text. In a similarity matrix, a reference article (an interest has been already expressed for it) correspond to a row, while the columns correspond to all the other articles for which the similarity was calculated. <ul> <li>Matrixes for our initial collection <ul> <li>similarity.PMRA.TA.profiles.TA.tsv: Similarity matrix for profiles.TA.tsv following the algorithm PMRA. This matrix is considered the baseline for further analyses</li> <li>similarity.PMRA.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm PMRA</li> <li>similarity.BM25_1.2_0.75.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm BM25 with k=1.2 and b=0.75</li> <li>similarity.COSINE.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm Cosine</li> </ul> </li> <li>Matrixes for our full-text collection <ul> <li>similarity.PMRA.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm PMRA</li> <li>similarity.PMRA.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm PMRA</li> <li>similarity.BM25.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm BM25 with k=1.2 and b=0.75</li> <li>similarity.BM25.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm BM25 with k= 1.2 and b= 0.75</li> <li>similarity.COSINE.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm Cosine</li> <li>similarity.COSINE.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm Cosine</li> </ul> </li> </ul> </li> <li>Correlation matrixes for similarities calculated for title-and-abstract taking as reference the similarity values obtained with PMRA for word stems on title-and-abstract. <ul> <li>pearsonCorrelation.PMRA.tsv: Correlation for similarity.PMRA.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.PMRA.tsv: Correlation for similarity.PMRA.profiles.PMID.tsv discriminated by TREC topics</li> <li>pearsonCorrelation.BM25_1.2_0.75.tsv: Correlation for similarity.BM25_1.2_0.75.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.BM25_1.2_0.75.tsv: Correlation for similarity.BM25_1.2_0.75.profiles.PMID.tsv discriminated by TREC topics</li> <li>pearsonCorrelation.COSINE.tsv: Correlation for similarity.COSINE.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.COSINE.tsv: Correlation for similarity.COSINE.profiles.PMID.tsv discriminated by TREC topics</li> </ul> </li> <li>Precision and recall summaries for the similarities calculated based on title-and-abstract. <ul> <li>StatsAllSummary.xlsx: Precision and recall at a global level, i.e., without considering TREC topics. This file includes information for BM25 with multiples values for constants k and b</li> </ul> </li> </ul> <p>Visualization for correlation matrixes as well as scattered plots for full-text based similarity is available at http://ljgarcia.github.io/semsim.benchmark</p>
Metabolomics Data Dictionary (Metabolon Inc. ) - from PMC articles (OA)
<p>A collection of metabolite and chemical names reported by Metabolon Inc. in the supplementary tables of open-access full-text articles from the PMC database.</p> <p><strong>Many entries can be duplicate because of chemical name variants reported in different articles. </strong></p>
biotea i-n dataset of rdf for pmc
<p>i-n dataset of rdf for pmc</p>
SoftwareKG-PMC
<p>SoftwareKG-PMC is a Knowledge Graph of Software mentions extracted from articles of the PMC Open Access Dataset.</p><p>Code for Construction and Analysis is provided at https://github.com/f-krueger/SoftwareKG-PMC-Analysis</p><p>This data set contains the CSV based representation of the SoftwareKG knowledge graph.</p><p>Note that the CSV files do not contain information about articles that where only available as pdf, so the overall number of articles considered here is reduced to 3036913.</p>
PMC_visualisation_and_source_data_SS_v1.0.7
<p>This is the first release of the code and data for:</p> <p>"Public health impact of current and proposed age-expanded perennial malaria chemoprevention: a modelling study"</p> <p>Swapnoleena Sen1,2, Lydia Braunack-Mayer3,4, Sherrie L Kelly1, Thiery Masserey1,2, Josephine Malinga4,5, Joerg J Moehrle2, Melissa A Penny4,5*</p> <p>1 Swiss Tropical and Public Health Institute, Allschwil, Switzerland<br>2 University of Basel, Basel, Switzerland<br>3 <span>Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland </span><br>4 Telethon Kids Institute, Nedlands, WA, Australia<br>5 Centre for Child Health Research, The University of Western Australia, Crawley, WA, Australia</p> <p>*Correspondence to: Prof Melissa A Penny (melissa.penny@uwa.edu.au)</p> <p>In this study, we integrated an individual-based model of malaria (OpenMalaria) with pharmacological models of drug action to assess the public health impact and cost-effectiveness of perennial malaria chemoprevention (PMC), and the added benefit of further age-expanded dosing schedule (referred as PMC+).</p> <p>The details of running OpenMalaria model, data generation and analysis (including R scripts used for preparing the source data files) for this study can be found in a separate "OpenMalaria_workflow_PMC_modeling" repository (DOI:10.5281/zenodo.12721515). </p> <p>Here the plotting functionalities are described. The repository is strcutured based on figures reported in the manuscript. Each figure has a folder as per its name that includes: 1) R code to plot figure 2) source data files and 3) one PNG and one PDF version of the figure. </p> <p>Please note: i) "dependencies.R" specifies all package information and dependencies in which the simulation, analysis scripts and plotting scripts are tested and stable. <br>ii) The R scripts rely on the folder structure and working directories used by the researchers. To replicate figures, you will need to adjust the file paths.</p>
Publication dates for PMC publications
<p>Lookup tables in plain JSON, mapping PMC publication identifiers to their earliest respective publication dates.</p> <p>The JSON files are archived in <code>pmc-publication-dates-by-identifier.tar.gz</code>. The archive contains files named after the first digit of the PMC publication identifiers they contain. E.g., the file <code>PMC1.json</code> will contain the data for identifiers <code>PMC1234567</code>, etc. Publication dates are given in the format <code>YYYY[-MM[-DD]]</code>, i.e., the earliest date is given with the maximum of information that has been available from the PMC OAI-PMH metadata, at least the publication year, if available also the publication month and day.</p> <h2>Reproducibility</h2> <p>The <a title="https://snakemake.readthedocs.io/" href="https://snakemake.readthedocs.io/">Snakemake</a> workflow that has produced this dataset has been archived and is available in <code>pmc-publication-dates-workflow.tar.gz</code>.</p> <h3>Running the workflow</h3> <p>To reproduce the dataset on a Linux machine, you need a version of the <a title="https://conda-forge.org/" href="https://conda-forge.org/"><code>conda</code></a> package manager installed on your system.</p> <p>Run the following:</p> <pre><code># Extract the archived workflow tar -xf pmc-publication-dates-workflow.tar.gz # Create conda environment from lock file conda env create -n pmc-metadata --file conda-environment.lock.yaml # Activate the environment conda activate pmc-metadata # Optionally, dry-run the workflow snakemake -n # Produce the output files snakemake --keep-storage-local-copies --software-deployment-method conda -c <NUMBER OF CORES TO USE> </code></pre> <h2>Workflow</h2> <p>To adapt/change the workflow, clone it from <a title="https://github.com/sdruskat/pmc-publication-metadata" href="https://github.com/sdruskat/pmc-publication-metadata">https://github.com/sdruskat/pmc-publication-metadata</a>. The workflow version used to produce this dataset is available at <a href="https://doi.org/10.5281/zenodo.11350802">https://doi.org/10.5281/zenodo.11350802</a>.</p>
FIGURE 12. Setosella cavernicola Harmelin, 1977. PMC. B26. 30.6.2018c in Revision of the genus Setosella (Bryozoa: Cheilostomata) with description of new species from deep-waters and submarine caves of the Mediterranean Sea
FIGURE 12. Setosella cavernicola Harmelin, 1977. PMC. B26. 30.6.2018c, Fara cave, Lesvos Island, 11–18 m depth, NE Aegean Sea, sampling station F4. a. Portion of a large, dead colony. b. Detail of an autozooid with the distal vibraculum and two large, disto-lateral opesiules. c. Cluster of autozooids with intramural budding. d. Close-up of the bottom-right colony portion in (a) Note the common regenerations in both autozooids and vibracula, some with closure plates. Scale bars: a = 500 μm; b = 100 μm; c, d = 200 μm.
FIGURE 11. Setosella cavernicola Harmelin, 1977. PMC. B26. 30.6.2018c in Revision of the genus Setosella (Bryozoa: Cheilostomata) with description of new species from deep-waters and submarine caves of the Mediterranean Sea
FIGURE 11. Setosella cavernicola Harmelin, 1977. PMC. B26. 30.6.2018c, Fara cave, Lesvos Island, 11–18 m depth, NE Aegean Sea, sampling station FC2. a. Large lobate colony exploiting the extremely uneven surface of concretion cavities. b. Close-up of the central part in (a) with some autozooids, one regenerated as a kenozooid. c. Autozooids from a dead colony portion. d. Cluster of fertile autozooids. e. Ancestrula with associated vibracula located distally and on the lateral left side. Scale bars: a = 1 mm; b, d, e = 200 μm; c = 100 μm.
FIGURE 10. Setosella cavernicola Harmelin, 1977. PMC. B26. 30.6.2018b in Revision of the genus Setosella (Bryozoa: Cheilostomata) with description of new species from deep-waters and submarine caves of the Mediterranean Sea
FIGURE 10. Setosella cavernicola Harmelin, 1977. PMC. B26. 30.6.2018b, Granchi cave, Plemmirio MPA, SE Sicily, W Ionian Sea, sample GR1P. a. View of a large, irregularly lobate colony. b. Periancestrular area with some autozooids showing an incompletely calcified cryptocyst. c. The bottom-right lobe in (a), developing from regeneration of some broken autozooids. d. Close-up of the regeneration line with irregular budding of autozooids and a kenozooid. e. Regenerated colony portion with the central fertile autozooid showing three subsequent intramural buddings. f. Anomalous budding of twin vibracula from a peripheral autozooid. g. Kenozooid (centre top) with a central, large, irregularly-shaped opesia. Scale bars: a, c = 500 μm; b, d–g = 200 μm.
PMC_visualisation_and_source_data_SS_v1.0.6_copy
<p>This is an erronous copy of the original submission of code and data for:</p> <p>"Public health impact of current and proposed age-expanded perennial malaria chemoprevention: a modelling study"</p> <p>Please refer to DOI <a href="https://doi.org/10.5281/zenodo.12722070">10.5281/zenodo.12722070 </a></p>
PheneBank: Processed Medline Abstracts and PMC full articles + Phenotype-Disease Associations
<p><strong>The PheneBank project:</strong></p> <p>Free text scientific literature has the potential to be an incredibly valuable source of data for uncovering the often hidden relationships between genes, diseases and phenotypes. Phenotypic descriptions cover abnormalities in anatomical structures, processes and behaviours. For example 'growth delay' and 'body weight loss'. Such descriptions form the basis for determining the existence and treatment of a disease but, because of their inherent complexity, have previously received less attention by the text mining community. In recent years, significant effort has been spent by a small number of expert curators to create coding systems for phenotypes (called "ontologies"), such as the Human Phenotype Ontology (HP) and the Mammalian Phenotype Ontology (MP). The PheneBank project proposes to support and speed up curation using terms discovered directly from the literature and to automatically integrate them with such standard ontologies. <br> <br> The project seeks to harness texts for extracting statistically significant associations between phenotypes, diseases and genes. Earlier approaches have suffered from not providing deep semantic representations of the phenotypes they tried to target. Our deep learning-based approach is an attempt to overcome this issue by reducing the uncertainty between textual and ontological forms of phenotypes. Specifically, the model treats multitoken named entities as a single token which allows more reliable handling of multiword expressions. The approach builds on ground breaking research at the European Bininformatics Institute by the PI (Nigel Collier) and the Co-investigator (Damian Smedley, Queen Mary University London), including terminology alignment of phenotypes using pairwise scoring of the conceptual elements that make up the phenotype. </p> <p><a href="http://www.phenebank.org">http://www.phenebank.org</a></p> <p><br> <strong>The dataset:</strong></p> <p>As an output of the PheneBank project, we release the set of 24 million MEDLINE abstracts as well as 3.8M open-access PMC full articles annotated with 9 classes of entity: Phenotype, Disease, Anatomy, Cell, Cell_line, GPR, Gene_variant, Molecule, and Pathway. The entities have been mapped to five major ontologies: SNOMED, HPO, MeSH, PRO, and FMA.</p> <p>In addition, we release the phenotype-disease associations that are automatically extracted based on co-occurrences statistics in Medline abstracts. Among different statistical measures we evaluated, the Fisher test best corresponded to the known tuples available from the curated associations available from the Monarch Initiative (https://monarchinitiative.org).</p> <p><br> <strong>Processing:</strong></p> <p>The NER tagging has been done using a BiLSTM-CRF neural model (<a href="https://github.com/pilehvar/phenebank">https://github.com/pilehvar/phenebank</a>) trained on expert-annotated data (to be released for research). The grounding to ontologies relies on semantic embedding of concepts and entities in a unified semantic space.</p> <p><br> <strong>Data format:</strong></p> <p><strong>PheneBank_Processed_PubMed.part[x].tar.gz </strong>contains 24,359,010 .txt files that are classified into 812 directories. Each .txt file is named with a PubMed article ID and contains the corresponding article's abstract and its annotations. The dataset is split into four (unequal) parts based on PubMed's structure:<br> part1: medline16n00* medline16n01* medline16n02* [299 directories, 2.8GB]<br> part2: medline16n03* medline16n04* [200 directories, 4.7GB]<br> part3: medline16n05* medline16n06* [200 directories, 5.3GB]<br> part4: medline16n07* medline16n08* [113 directories, 3.1GB]</p> <p>The <strong>PheneBank_Processed_PMC.tar.gz</strong> files has 6,180 directories which are named after the journal titles from which the articles have been drawn. There are three files per each article (i.e., 3 .txt files for the 3,751,770 distinct articles), containing text from different parts of the article: .title.txt, .abstract.txt, and .body.txt. </p> <p>Each line starts with a word; for those words that are identified as entities, entity type and mapping information are followed in the same line (tab separated), with the following format:</p> <p>word <TAB> ::: <TAB> entity_type <TAB> entity_concept_ID_1##confidence_score_1 entity_concept_ID_2##confidence_score_2 ...</p> <p>Note that the concepts are sorted according to their mapping confidence scores.</p> <p><br> As for the <strong>PheneBank_Associations.tsv</strong> file, there are ten columns that correspond to the following (left to right):</p> <p>- Disease Name<br> - Disease (MONDO) ID<br> - Phenotype Name<br> - Phenotype (HPO) ID<br> - Co-occurrence Frequency<br> - Disease Frequency<br> - Phenotype Frequency<br> - Fisher (log)<br> - Dice<br> - Normalized PMI</p> <p> </p> <p> </p>
Perennial Malaria Chemoprevention (PMC) in Cameroon
ClinicalTrials.gov study NCT05889052. IPD Sharing: NO. Countries: 1. Publications: 1.
Post-discharge Malaria Chemoprevention(PMC) Study
ClinicalTrials.gov study NCT02671175. IPD Sharing: YES. Countries: 2. Publications: 2.
Perennial Malaria Chemoprevention (PMC) in Côte D'Ivoire
ClinicalTrials.gov study NCT05856357. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PMC Split Preparation for Colon Cleansing
ClinicalTrials.gov study NCT01909219. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Journal based analysis of the Open Access Europe PMC articles.
<p>This file shows the 20 most-represented journals that tag data citations in the XML within articles in the Open Access set from Europe PMC (May 2013). Secondly, it shows the 20 most-represented journals according to number of individual articles, demonstrating that the journals that undertake tagging of data citations most frequently are not the same as those that occur most frequently.</p>
Next-generation sequencing facilitates quantitative analysis of wild-type peritoneal murine mast cells (PMCs) and their comparison to PMC lines
GEO Series GSE227065. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Study to Assess Safety and Tolerability of PMC-403 in Subjects With Neovascular Age-related Macular Degeneration
ClinicalTrials.gov study NCT05953012. IPD Sharing: NO. Countries: 1. Publications: 0.
Long-term Follow-up of Patients With Alcoholic Liver Cirrhosis Who Had Administered Cellgram-LC in PMC-P-07 Study
ClinicalTrials.gov study NCT05093881. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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