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Dataset results
206 results for “Molecular biology”
Dataset for "Evaluation of Publicly Available Information on Sex-related Differences in the Efficacy and Safety of New Molecular Entities and Therapeutic Biological Products"
<p>Contains our extraction sheets with additional documents/notes on methods used in our study.</p>
Molecular and biological characterization of an Asian-American isolate of Chikungunya Virus
<p>This is a dataset of the figures used in the development of the manuscript <strong>Molecular and biological characterization of an Asian-American isolate of Chikungunya Virus</strong></p> <p> </p>
Fig. 8 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 8. Phenotypic characters mapped against broad molecular phylogenies of haemosporidian parasites. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.
Fig. 7 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 7. Phenotypic characters mapped against broad molecular phylogenies of haemogregarine parasites. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.
Fig. 6 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 6. Phenotypic characters mapped against broad molecular phylogenies of haemococcidian parasites (blood-borne genera shown in red). Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 5. Developmental cycles and hosts for apicomplexan blood parasites (DH = definitive host; IH = intermediate host; PH = paratenic host; bm = blood meal; bmi = injected during blood meal; ve = vector eaten).
Fig. 3 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 3. Developmental stages formed by kinetoplastid flagellates (blood-borne genera shown in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 2. Geological time periods with milestones in the development of life on Earth, together with historical extent of fossil records for particular assemblages.
Fig. 10 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 10. Probable evolutionary origins of haemoprotozoan parasites (solid lines = strong inferential support; dotted lines = presumptive).
Fig. 9 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 9. Phenotypic characters mapped against broad molecular phylogenies of piroplasm blood parasites. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.
Fig. 4 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 4. Phenotypic characters mapped against broad molecular phylogenies of trypanosomatid haemoflagellates. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.
Partitioned Image Data for Machine Learning Analysis of Molecular Biology Figures
<p><strong> Corpus Composition</strong></p> <p>This data collection provides four types of hand-curated images from open access research articles images. The types are:</p> <ol> <li>chart (n=811): data displays such as bar charts, scatterplots, line graphs, etc.</li> <li>diagram (n=816): any general conceptual diagram</li> <li>gel (n=1182): the output of electrophoresis experiments in Northern, Western, or Southern Blot experiments. </li> <li>histology (n=3458): microscope images of tissue with histological staining</li> </ol> <p>The images are simply organized in subdirectories as individual files. File names are based on PubMed Id and Figure number. </p>
Molecular Biology Open Access Pubmed Word and Sentence Representations
<p><strong>Natural Language Embeddings about Molecular Biology</strong></p> <p>This dataset is concerned with developing a tailored training data set for word and sentence embedding based on biomedical text that has some component associated with molecular work (as opposed to the other range of work indexed in PubMed like non molecular clinical work, studies of human behavior, etc). </p> <p><strong>Raw Data</strong></p> <p>In order to develop natural language embeddings (for words and sentences), we queried PMC and MEDLINE for molecular papers only by using high-level MeSH terms to restrict interest to papers with a molecular focus. We used the following MeSH terms:</p> <ul> <li>Cells [A11]</li> <li>Multiprotein Complexes [D05.500]</li> <li>Protein Aggregates [D05.875]</li> <li>Hormones [D06]</li> <li>Enzymes and Coenzymes [D08]</li> <li>Carbohydrates [D08]</li> <li>Lipids [D10]</li> <li>Amino Acids, Peptides and Proteins [D12]</li> <li>Nucleic Acids, Nucleotides and Nucleosides [D13]</li> <li>Biological Factors [D23]</li> <li>Pharmaceutical Preparations [D26]</li> <li>Metabolism [G03]</li> <li>Genetic Phenomena [G06]</li> </ul> <p>Queries for these terms use the following string:</p> <blockquote> <p>"cells"[MeSH Terms] OR "Multiprotein Complexes"[mh] OR "Protein Aggregates"[mh] OR "Hormones, Hormone Substitutes, and Hormone Antagonists"[mh] OR "Enzymes and Coenzymes"[mh] OR "Carbohydrates"[mh] OR "Lipids"[mh] OR "Amino Acids, Peptides, and Proteins"[mh] OR "Nucleic Acids, Nucleotides, and Nucleosides"[mh] OR "Biological Factors"[mh] OR "Pharmaceutical Preparations"[mh] OR "Metabolism"[mh] OR "Cell Physiological Phenomena"[mh] OR "Genetic Phenomena"[mh]</p> </blockquote> <p>PubMed returns 11,447,521 abstracts. PMC returns, 1,720,266 documents, 509,722 of these are open access. We downloaded, parsed and concatenated 403,825 PMC open access documents into a single file `molecular_oa_pmc.tsv`. This is a 33GB TSV file with the following columns:</p> <ul> <li>File:Paragraph - a unique identifier for each paragraph</li> <li>SentenceId - the local number of the sentence in the document</li> <li>Sentence Text - tokenized text of the sentence (based on <a href="https://github.com/ClearTK/cleartk/blob/master/cleartk-token/src/main/java/org/cleartk/token/tokenizer/TokenAnnotator.java">ClearTk's TokenAnnotator.java</a>)</li> <li>Codes - <code>exLink</code> for the presence of a citation, <code>inLink</code> for the presence of link to a Figure</li> <li>Figures - Figure codes</li> <li>Headings - High level section of the paper</li> <li>Offset_Begin - offset of the start of the sentence within the paper</li> <li>Offset_End - offset of the start of the sentence within the paper</li> </ul> <p>We repeated the same process for PubMed abstracts to generate a 3.6G file (`molecular_oa_medline.tsv`) with three columns:</p> <ol> <li>Pubmed ID</li> <li>A Boolean value indicating whether the article is a review</li> <li>Text</li> </ol> <p>We concatenated the text columns of these two files into a single 30GB file (`molecular_oa.txt`) where each line is a single sentence and the text is fully tokenized. </p> <p>These three files are archived in `molecular_oa_raw_text.tar.gz`.</p> <p><strong>Fasttext Embedding</strong></p> <p>We trained a fasttext model on the raw training data (https://fasttext.cc/) using the standard `skipgram` parameter. A gzipped copy of the word embeddings is included in `fasttext.model.vec.gz` </p> <p><strong>GloVe Embedding</strong></p> <p>We trained GloVe models on the raw training data (https://nlp.stanford.edu/projects/glove/). A gzipped copy of the best performing word embeddings is included in `bio_GloVe_300.tar.gz` </p>
Linked collectors and determiners for: European Molecular Biology Laboratory Australian Mirror.
Natural history specimen data linked to collectors and determiners held within, "European Molecular Biology Laboratory Australian Mirror". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c1fc2df7-223b-4472-8998-70afb3b749ab">https://bionomia.net/dataset/c1fc2df7-223b-4472-8998-70afb3b749ab</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c1fc2df7-223b-4472-8998-70afb3b749ab">https://gbif.org/dataset/c1fc2df7-223b-4472-8998-70afb3b749ab</a>. Formatted as a Frictionless Data package.
Supporting data files for "Binding of Biologically Relevant Divalent Cations to Aqueous Carboxylates: Molecular Simulations Guided by Raman Spectroscopy"
<p>Parameter files and typical simulation input files that allow replication of the computational work presented in the paper "Binding of Biologically Relevant Divalent Cations to Aqueous Carboxylates: Molecular Simulations Guided by Raman Spectroscopy", authored by Denilson Mendes de Oliveira, Samual R. Zukowski, Vladimir Palivec, Jérôme Hénin, Hector Martinez Seara, Dor Ben Amotz, Pavel Jungwirth and Elise Duboué-Dijon</p>
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
Advanced Molecular Dynamics Model for Investigating Biological-Origin Microfibril Structures
<p>This data contains all necessary input file to construct the micro fibril.</p>
Molecular biology techniques for fungal identification: focus on Aspergillus spp.
<p><span>This tutorial summarises the main steps to identify through a molecular approach <em>Aspergillus</em> species isolated from food samples. It starts with introducing the <em>Aspergillus</em> genus, its economic and medical importance, and latest updates in taxonomy. The polyphasic approach for <em>Aspergillus</em> identification was introduced based on Samson et al., 2014. An overview on each of the stage to conduct molecular identification is provided, including examples of different DNA extraction protocols, PCR analysis with <em>Aspergillus</em> species recommended primers, Sanger’s sequencing, and interpretation of BLAST results.</span></p>
Fig. 1 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 1. Key characteristics of the five haemoprotozoan assemblages.
Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study
<p>GenBank accession numbers for nuclear sequence records used to build phylogenetic tree in manuscript entitled "Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study". Nuclear sequences and GenBank accession numbers originally from multigene alignment and supplementary material published in Rabosky et al. (2018; 2019).</p> <p>Rabosky, D.L., Chang, J., Title, P.O. <em>et al.</em> An inverse latitudinal gradient in speciation rate for marine fishes. <em>Nature</em> <strong>559</strong>, 392–395 (2018). https://doi.org/10.1038/s41586-018-0273-1</p> <p>Rabosky, Daniel L. et al. (2019), Data from: An inverse latitudinal gradient in speciation rate for marine fishes, Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.fc71cp4">https://doi.org/10.5061/dryad.fc71cp4</a> </p>
ScienceDex guides
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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.