Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “Information discovery”
Examining LGBTQ+-related Concepts in the Semantic Web: Link Discovery, Concept Drift, Ambiguity, and Multilingual Information Reuse
<div> <h1>Examining LGBTQ+-related Concepts in the Semantic Web</h1> </div> <div> <h2>Introduction</h2> </div> <p>Welcome to the project. We study the links between LGBTQ+ ontologies and structured vocabularies. More specifically, we focus on GSSO, Homosaurus, QLIT, and Wikidata. The code is free for use with the license GPL 3,0. You can resue/extend the code for free as long as you give credits to us in your publication/data. Citation information will be added after the corresponding paper gets accepted. The paper is under submission and will be included soon. </p> <p>If you would like to extend this work, you may want to contact the experts in the acknowledgement before releasing your data/code about legal and ethical issues. The DOI for this version is 10.5281/zenodo.12684870. The latest code can be found at https://github.com/Multilingual-LGBTQIA-Vocabularies/Examing_LGBTQ_Concepts. </p> <p>To reproduce the results or extend our work, you need to take the following steps.</p> <div> <h2>Step 1: Preparing the data</h2> </div> <p>In this project, the following datasets were used:</p> <ul> <li>QLIT: version 1.0</li> <li>Homosaurus: version 3.5 and version 2.3</li> <li>Wikidata: retrieved from the SPARQL Endpoint (<a href="https://query.wikidata.org/sparql" rel="nofollow">https://query.wikidata.org/sparql</a>) and processed between 5th May and 8th May, 2024.</li> <li>GSSO: we used gsso.owl (version 2.0.10) obtained from its Github (<a href="https://github.com/Superraptor/GSSO">https://github.com/Superraptor/GSSO</a>).</li> <li>LCSH was obtained from the official website: <a href="https://id.loc.gov/authorities/subjects.html" rel="nofollow">https://id.loc.gov/authorities/subjects.html</a> on 9th May, 2024. The LCSH data was converted to its HDT format.</li> </ul> <p>Please put the corresponding files in the following folders (and change its names where necessary) to make sure that the Python scripts can find your code.</p> <ul> <li>./data/GSSO/gsso.owl</li> <li>./data/Homosaurus/v2.ttl and ./data/Homosaurus/v3.ttl</li> <li>./data/LCSH/lcsh.hdt (we used its HDT format for fast query and analysis). The original file is also attached: subjects.skosrdf.nt.</li> <li>./data/QLIT/Qlit-v1.ttl</li> </ul> <p>The case of Wikidata is more complicated. The following scripts were used for the retrival of data. These scripts are all in the folder ./data/wikidata/</p> <ul> <li>We used the Wikidata SPARQL endpoint: <a href="https://query.wikidata.org/" rel="nofollow">https://query.wikidata.org/</a></li> </ul> <p>The following relations from Wikidata were used while extracting triples.</p> <ul> <li>Wikidata - GSSO: <a href="http://www.wikidata.org/prop/direct/P9827" rel="nofollow">http://www.wikidata.org/prop/direct/P9827</a></li> <li>Wikidata - Homosaurus 2: <a href="http://www.wikidata.org/prop/direct/P6417" rel="nofollow">http://www.wikidata.org/prop/direct/P6417</a></li> <li>Wikidata - Homosaurus 3: <a href="http://www.wikidata.org/prop/direct/P10192" rel="nofollow">http://www.wikidata.org/prop/direct/P10192</a></li> <li>Wikidata - LCSH: <a href="http://www.wikidata.org/prop/direct/P244" rel="nofollow">http://www.wikidata.org/prop/direct/P244</a></li> </ul> <p>The generated files are:</p> <ul> <li>'wikidata-homosaurus-v2-links.nt'</li> <li>'wikidata-homosaurus-v3-links.nt'</li> <li>'wikidata-gsso-links.nt'</li> <li>'wikidata-qlit-links.nt'</li> <li>'wikidata-lcsh-links-all.nt'</li> </ul> <p>Please note that the case of Wikdiata-LCSH is more complicated: there are so many links that are nothing to do with the entities in our scope. We restrict it to only entities in the scope of this paper. See below for more details.</p> <p>You can find all the scripts in the corresponding folder in the data folder.</p> <p>All the SPARQL queries used can be found in the folder ./SPARQL/</p> <p>Note! For GSSO, the following two mistakes were corrected while preprocessing:</p> <ul> <li><a href="https://www.wikidata.org/wiki/Q1823134" rel="nofollow">https://www.wikidata.org/wiki/Q1823134</a> should not be used as a relation. We have replaced it with <a href="http://www.wikidata.org/prop/direct/P244" rel="nofollow">http://www.wikidata.org/prop/direct/P244</a>.</li> <li>Instead of referring to the page, we refer to the entity. We use <a href="http://www.wikidata.org/entity/" rel="nofollow">http://www.wikidata.org/entity/</a>* instead of <a href="https://www.wikidata.org/wiki/" rel="nofollow">https://www.wikidata.org/wiki/</a>*</li> </ul> <p>The redirection test was conducted on 30th April, 2024, between 6PM and 8PM. The files can be found in the folder of ./data/Homosaurus/redirect/.</p> <div> <h2>Integrating the data</h2> </div> <p>In the folder ./integrated_data/, you can find all the scripts related to the integrated data. Unfortunately, due to the CC-BY-NC-ND license of GSSO and Homosaurus, the integrated data will not be made available. But you can generate it with the instructions above and by using the following scripts.</p> <p>The script ./integrated_data/integrate.py takes advantage of the data generated. It first integrates a list of files of links. Then we go through the links between Wikidata and LCSH. Only those that are in the scope of the study are included.</p> <ul> <li>If your steps are correct and using the same version as we did, you should be able to get four files:</li> <li>a) the integrated file as integrated.nt</li> <li>b) the links that are relevant for this study: wikidata-lcsh-links-selected.nt.</li> <li>c) a plot of the distribution of the size of WCCs</li> <li>d) a mapping of entities and their corresponding ID of WCCs.</li> </ul> <div> <h2>Weakly Connected Components</h2> </div> <p>The weakly connected components (WCCs) were computed for the following three purposes:</p> <p>a) Discovering missing links. See the section below for details.</p> <p>b) The WCCs can be used for manual examination. These are entities that form clusters about related concepts. The intuition is that the larger they are, the more likely there is concept drift/change, ambiguity, and mistakes.</p> <p>c) Multilingual information reuse. Smaller WCCs with exactly one entity from each dataset (e.g. Homosaurus and Wikidata) can then be used to suggest labels for the one with fewer labels for some given languages. See below for more details.</p> <p>As mentioned above, the distribution has been plotted. You can find this plot here: ./integrated_data/frequency.png</p> <p>In the folder ./integrated_data/weakly_connected_components/, you can find all the WCCs and their links.</p> <p>Two examples were given in the folder. The largest WCC about sex, gender, fucking, etc. The other is about BDSM and fetish.</p> <div> <h2>Discovering missing and outdated links</h2> </div> <p>Taking advantage of WCCs, we can further find missing and outdated links. The scripts are in the folder ./discover_missing_links.</p> <p>Three examples were given. The first two is about discovering missing links. The last one is about finding outdated links.</p> <ul> <li> <p>The script ./discover_missing_links/discover_H3_LCSH.py and ./discover_missing_links/discover_QLIT_LCSH.py are scripts that outputs links that could be missing in Homosaurus and QLIT respectively. This was computed by looking at the WCCs. If two entities are both involved in the same WCC, there could be a link between them. The csv files in the same folder are the corresponding links found.</p> </li> <li> <p>The script ./discover_missing_links/find_qlit_outdated_links/ is used to discover the outdated links between QLIT and Homosaurus v3. There was only one link found.</p> </li> <li> <p>The 105 potentially missing links were taken for further review by Swedish-speaking experts from the QLIT team, which showed that 78 (72.38%) suggested links should be included: 38 (36.19%) can be included using skos:exactMatch and another 38 (36.19%) using skos:closeMatch. 28 (26.67%) suggested links are incorrect. The manual annotation are included in the file ./discover_missing_links/Annotated_found_new_links_qlit-lcsh.xlsx.</p> </li> </ul> <div> <h2>Multilingual Information Reuse</h2> </div> <p>You can find two attempts in the folders about the use of GSSO and Wikidata for Homosaurus respectively.</p> <ul> <li>./WCC-based-gsso-multilingual_info_reuse/</li> <li>./WCC-based-wikidata-multilingual_info_reuse/</li> </ul> <p>Additionally, we provide also some code for the reuse of Wikidata multilingual info for QLIT. It's in the folder</p> <ul> <li>./WCC-based-QLIT-info-reuse-from-Wikidata/</li> </ul> <p>They follow very similar steps:</p> <ol> <li> <p>Compute the one-to-one mapping using the WCCs. The script is named compute-one-to-one-mapping.py</p> </li> <li> <p>Extract the multilingual labels from sources. The corresponding file is extract_multilingual_labels_from_one_to_one_mappings.py</p> </li> <li> <p>Provide the extracted multilingual as suggestions for targeting entities. The name of the corresponding files are like "*suggesting-labels.py", where the * is replaced by the actual source/target.</p> </li> </ol> <p>For GSSO, we use the following relations:</p> <ul> <li><a href="http://www.w3.org/2000/01/rdf-schema#label" rel="nofollow">http://www.w3.org/2000/01/rdf-schema#label</a></li> <li><a href="http://www.geneontology.org/formats/oboInOwl#hasRelatedSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasRelatedSynonym</a></li> <li><a href="http://www.geneontology.org/formats/oboInOwl#hasSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasSynonym</a></li> <li><a href="http://www.geneontology.org/formats/oboInOwl#hasExactSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasExactSynonym</a></li> <li><a href="http://purl.org/dc/terms/replaces" rel="nofollow">http://purl.org/dc/terms/replaces</a></li> <li><a href="https://www.wikidata.org/wiki/Property:P5191" rel="nofollow">https://www.wikidata.org/wiki/Property:P5191</a></li> <li><a href="https://www.wikidata.org/wiki/Property:P1813" rel="nofollow">https://www.wikidata.org/wiki/Property:P1813</a></li> <li><a href="https://schema.org/alternateName" rel="nofollow">https://schema.org/alternateName</a></li> <li><a href="http://www.w3.org/2002/07/owl#annotatedTarget" rel="nofollow">http://www.w3.org/2002/07/owl#annotatedTarget</a></li> </ul> <p>Additioinally, we found the relation to be studied in the future: <a href="http://www.geneontology.org/formats/oboInOwl#hasNarrowSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasNarrowSynonym</a></p> <p>For Wikidata, there are only two:</p> <ul> <li><a href="http://www.w3.org/2000/01/rdf-schema#label" rel="nofollow">http://www.w3.org/2000/01/rdf-schema#label</a></li> <li><a href="http://www.w3.org/2004/02/skos/core#altLabel" rel="nofollow">http://www.w3.org/2004/02/skos/core#altLabel</a></li> </ul> <div> <h2>Additional analysis</h2> </div> <p>Additionally, we perform an analysis using only redirection and replacement for GSSO and Homosaurus. The scripts are in the folder ./additional_test_gsso_multilingual_info_reuse. We consider also Homosaurus v2. This additional analysis shows the following:</p> <ul> <li> <p>For the Turkish language, in total there are 103 triples about labels about 23 entities. The average suggested labels per entity is 3.0.</p> </li> <li> <p>For the Spanish language, in total there are 205 triples about labels about 43 entities. The average suggested labels per entity is 2.12.</p> </li> <li> <p>For the French language, in total there are 277 triples about labels about 47 entities. The average suggested labels per entity is 2.19.</p> </li> <li> <p>For the Danish language, in total there are 115 triples about labels about 47 entities. The average suggested labels per entity is 2.70.</p> </li> </ul> <p>Some analysis about the replacement relations of Homosaurus is in the folder ./data/Homosaurus/replace_relations_homosaurus/.</p> <p>Finally, some additional analysis is included in the folder ./analysis_integrated_graph. Currently, there is only one that is about outdated entities in Homosaurus v3. Some more analysis will be added in the future.</p> <div> <h2>Acknowledgement</h2> </div> <p>The authors appreciate the help of the following researchers:</p> <ul> <li>Siska Humlesjö, QLIT, Göteborgs Universitet (<a href="mailto:siska.humlesjo@lir.gu.se">siska.humlesjo@lir.gu.se</a>)</li> <li>Olov Kriström, former member of QLIT</li> <li>Jack van der Wel, IHLIA (<a href="mailto:jack@ihlia.nl">jack@ihlia.nl</a>)</li> <li>Clair Kronk, GSSO (<a href="mailto:clair.kronk@mountsinai.org">clair.kronk@mountsinai.org</a>)</li> </ul> <div> <p>If you would like to extend this work, you may want to contact them before releasing your data/code about legal and ethical issues.</p> <h2>Contact</h2> </div> <ul> <li>Shuai Wang, Vrije Universiteit Amsterdam (<a href="mailto:shuai.wang@vu.nl">shuai.wang@vu.nl</a>)</li> <li>Maria Adamidou, Vrije Universiteit Amsterdam (<a href="mailto:m.adamidou@student.vu.nl">m.adamidou@student.vu.nl</a>)</li> </ul> <p> </p> <p>Thank you very much for your interest in our project!</p>
Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis
<p>We performed comprehensive epigenetic profiling in immune cell samples from patients with ankylosing spondylitis and healthy controls. <br><br><em>Note: Due to Zenodo updating their maximum file limit to 100 files, version 4 </em>(v4) <em>of this archive has been split into 5 compressed archive (tar.gz) files containing all previous and additional files. <br><br></em>Version 4 of this archive (updated 03/06/2025) adds 4 files to the archive that were omitted in previous versions which have now been made available. These were: <em><br></em></p> <ul> <li>RNA_CD8_raw_counts.txt.gz</li> <li>RNA_CD8_normalised_counts.txt.gz</li> <li>RNA_CD14_raw_counts.txt.gz</li> <li>RNA_CD14_normalised_counts.txt.gz </li> </ul> <p><em>--------------------------------------------------------------------------------------------------------------------------</em></p> <p><strong>RNA-seq/ATAC-seq/ChIPm/eRNA: </strong>Raw and normalised count data for each gene or epigenetic peak in CD4+ T cells, CD8+ T cells, and CD14+ monocytes from AS patients and healthy controls. File name is in the format: "modality_cell-type_raw/normalised_counts.txt.gz". Table S2 shows which experiments were performed on which samples. </p> <p>This data can be found in the "Raw_Counts.tar.gz" and "Normalised_Counts.tar.gz" archives. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>ChromHMM: </strong>We used ChromHMM to integrate epigenomic data into a 14-emission state model detailing chromatin functionality in AS patients and healthy controls. ChromHMM filenames are in the format: "ChromHMM_sampleID_celltype_n.bed.gz" where n is the number of states in the ChromHMM emission model.</p> <p>This data can be found in the "ChromHMM_AS_HV.tar.gz" archive. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Capture-C: </strong>We performed Capture-C to detect chromosome looping interactions between gene promoters and SNPs associated with ankylosing spondylitis. Capture-C count data are shown in the format: "CaptureC_celltype_gene_Pro/SNP_normalised.unionbdg". We used PeakY to calculate a score for each interaction. PeakY scores are given in the following format: "PeakY_AS/HV_celltype_tier_chrloc_gene_Pro/SNP.txt". gene_Pro and gene_SNP relate to the baitsets given in Table S8.</p> <p>This data can be found in the "CapC_Count_Data.tar.gz" and "PeakY_regions.tar.gz" archives. </p>
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
Biologically informed machine learning for identifying human placental cellular heterogeneity and preeclampsia discovery
<p>Single-cell transcripts of 20,518 placental cells and identified 12 major placental cell clusters were collected from the European Bioinformatics Institute (EBI; accession no. EGAS00001002449) (29). Based on the same parameters, we clustered and visualized highly similar cells using t-distribution random neighbourhood embedding (T-SNE) to identify 17 cell subpopulations. we selected nine placental cell clusters that have received more attention from biologists for our study according to the literature survey (Table 1). Considering the sample balance, 7178 single-cell transcriptome data were used to identify human placental cell subpopulations. The samples were randomly divided into a 4809-sample training set and a 2369-sample testing set. The same strategy was applied to split single-cell transcriptomic datasets from healthy and preeclampsia patients (EBI; accession no. EGAS00001002449), with 9852 samples (healthy 4705, preeclampsia 5147) in the training set and 5305 samples (healthy 2473, preeclampsia 2832) in the independent test set</p>
Data from: Discovery and information-theoretic characterization of transcription factor binding sites that act cooperatively
Transcription factor binding to the surface of DNA regulatory regions is one of the primary causes of regulating gene expression levels. A probabilistic approach to model protein–DNA interactions at the sequence level is through position weight matrices (PWMs) that estimate the joint probability of a DNA binding site sequence by assuming positional independence within the DNA sequence. Here we construct conditional PWMs that depend on the motif signatures in the flanking DNA sequence, by conditioning known binding site loci on the presence or absence of additional binding sites in the flanking sequence of each siteʼs locus. Pooling known sites with similar flanking sequence patterns allows for the estimation of the conditional distribution function over the binding site sequences. We apply our model to the Dorsal transcription factor binding sites active in patterning the Dorsal–Ventral axis of Drosophila development. We find that those binding sites that cooperate with nearby Twist sites on average contain about 0.5 bits of information about the presence of Twist transcription factor binding sites in the flanking sequence. We also find that Dorsal binding site detectors conditioned on flanking sequence information make better predictions about what is a Dorsal site relative to background DNA than detection without information about flanking sequence features.
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
The best of both worlds: highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration
<p><span>Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry is used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.</span></p>
Supplementary information provided with Murray et al.: Discovery of an Antarctic ascidian-associated uncultivated Verrucomicrobia with antimelanoma palmerolide biosynthetic potential
<p><span>The Antarctic marine ecosystem harbors a wealth of biological and chemical innovation that has risen in concert over millennia since the isolation of the continent and formation of the Antarctic circumpolar current. Scientific inquiry into the novelty of marine natural products produced by Antarctic benthic invertebrates led to the discovery of a bioactive macrolide, palmerolide A, that has specific activity against melanoma and holds considerable promise as an anticancer therapeutic. While this compound was isolated from the Antarctic ascidian <i>Synoicum adareanum</i>, its biosynthesis has since been hypothesized to be microbially mediated, given structural similarities to microbially-produced hybrid non-ribosomal peptide-polyketide macrolides. Here, we describe a metagenome-enabled investigation aimed at identifying the biosynthetic gene cluster (BGC) and palmerolide A-producing organism. A 74 Kbp candidate BGC encoding the multi-modular enzymatic machinery (hybrid Type I-<i>trans</i>-AT polyketide synthase-non-ribosomal peptide synthetase and tailoring functional domains) was identified and found to harbor key features predicted as necessary for palmerolide A biosynthesis. Surveys of ascidian microbiome samples targeting the candidate BGC revealed a high correlation between palmerolide-gene targets and a single 16S rRNA gene variant (R=0.83 – 0.99). Through repeated rounds of metagenome sequencing followed by binning contigs into metagenome-assembled genomes, we were able to retrieve a near-complete genome (10 contigs) of the BGC-producing organism, a novel verrucomicrobium within the <i>Opitutaceae</i> family that we propose here as <i>Candidatus</i> Synoicihabitans palmerolidicus. The refined genome assembly harbors five highly similar BGC copies, along with structural and functional features that shed light on the host-associated nature of this unique bacterium.</span></p>
FIGURE. Map of specimens collected for the phylogenetic analysis in this study, excluding Tulipa iliensis and T. altaica, which both lacked GPS information. Populations of the new species T. toktogulica are labelled in order of discovery. in Tulipa toktogulica (Liliaceae), a cryptic, endangered new species from the western Tien-Shan, Kyrgyzstan
FIGURE. Map of specimens collected for the phylogenetic analysis in this study, excluding Tulipa iliensis and T. altaica, which both lacked GPS information. Populations of the new species T. toktogulica are labelled in order of discovery.
Data from: Discovery and information-theoretic characterization of transcription factor binding sites that act cooperatively
Open the record for dataset details and reuse information.
Supplementary information provided with Murray et al.: Discovery of an Antarctic ascidian-associated uncultivated Verrucomicrobia with antimelanoma palmerolide biosynthetic potential
Open the record for dataset details and reuse information.
Supporting Information for Recommender Systems in Antiviral Drug Discovery
<p>Supporting Information for Recommender Systems in Antiviral Drug Discovery</p>
ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'
Open the record for dataset details and reuse information.
Supporting Information: High-Throughput Discovery of Electrides
<p><strong>High-Throughput Discovery of Electrides</strong></p> <p>This repository hosts part of the results of chapter 5 of the PhD thesis ("Theory and Discovery of Electride Materials) of Chengcheng Xiao at Imperial College London. Specifically, it contains the high-throughput screening results of electride materials performed using computational approaches.</p> <p>The results are stored in the form of a pickled pandas DataFrame (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_pickle.html) with pickle protocol 3 (https://docs.<br> python.org/3/library/pickle.html). The results (Electride_Screening.pkl) can be read using the following Python code:</p> <pre><code class="language-python">import pandas as pd mpdf=pd.read_pickle('Electride_Screening.pkl')</code></pre> <p> </p> <p>The keys used and their meanings are listed here:</p> <p>- material_id: Identification string from the Materials Project.<br> - incar: VASP-INCAR file.<br> - poscar: VASP-POSCAR file.<br> - potcar: VASP-POTCAR (header only) file.<br> - kpoints: VASP-KPOINTS file for HTP screening.<br> - WITH_ELF_MAX: Has local ELF maxima with > 3 neighbors?<br> - MAX_ELF: The value of the identified ELF maxima.<br> - ELF_LAPLACE: Laplacian of the ELF at the identified site.<br> - DIST2ATOM: Distance from identified site to surrounding atoms.<br> - MAX_OCC: The occupation associated with the identified site.<br> - ATOM_TYPE: The type of atoms surrounding the identified site.<br> - NUM_ATOM: The number of atoms surrounding the identified site.<br> - MAG_SYS: Is the system magnetic?<br> - INTERSTITIAL_STRUCT: Structure file with interstitial site labeled as Z.<br> - SPIN_CHANNEL: If is a magnetic system, which spin channel is used?<br> - WITH_CHG_MAX: Has local charge maxima?<br> - MAXOCC_NORMALIZED: Occupation normalized to be between 0 and 2.<br> - Ze: figure of merit [(MAXOCC_NORMALIZED/2+MAX_ELF)/2].</p>
Comparison of viral RNA–host protein interactomes across pathogenic RNA viruses informs rapid antiviral drug discovery for SARS-CoV-2
GEO Series GSE181866. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
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.