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1,923 results for “compounding”
Spectra of linear and quadratic magnetooptic parameters of Co2MnSi Heusler compound
<p>Dataset contain spectra of permitivity in 0th order in magnetization (eps_d) , of linear magnetooptic parametr <em>K</em> and of quadratic magnetooptic parameters <em>G<sub>s</sub></em> and <em>2G<sub>44</sub></em>. All those parameters fully describe perrmitivity tensor of Co<sub>2</sub>MnSi Heusler compound up to second order in magnetization. This dataset is discussed in the article: <em>Scaling of quadratic and linear magnetooptic Kerr effect spectra with L2<sub>1 </sub>ordering of Co<sub>2</sub>MnSi Heusler compound</em></p>
Heatmaps of quantitative and qualitative phenotypes of zebrafish pronephroi upon compound exposure
<p>Heatmaps of quantitative and qualitative phenotypes of embryonic zebrafish pronephroi after exposure to compounds from the Prestwick library.</p> <p>For further details please see:</p> <p><em>Westhoff JH, Steenbergen PJ, Thomas LSV, Heigwer J, Bruckner T, Cooper L, Tönshoff B, Hoffmann GF and Gehrig J (2020) In vivo High-Content Screening in Zebrafish for Developmental Nephrotoxicity of Approved Drugs. Front. Cell Dev. Biol. 8:583. doi: 10.3389/fcell.2020.00583</em></p> <p>The images represent full resolution versions of the thumbnails presented in: </p> <ol> <li>Supplementary Figure 3 | Fully annotated heat map of quantitative features.</li> <li>Supplementary Figure 4 | Fully annotated heat map of qualitative features.</li> </ol> <p> </p> <p> </p>
Volatile organic compound analysis, a new tool in the quest for preterm birth prediction – an observational cohort study
<p>Vaginal swabs were taken in pregnancy in high risk asymptomatic women attending a preterm prevention clinic. Women in the study attended the clinical due to a history of preterm birth or midtrimester pregnancy loss, or due to a history of cervical surgery. Individualised management plans were made depending upon individual patient risk factors. During their attendance to the clinic vaginal swabs were taken for VOC analysis. Swabs were taken between 15 and 28 weeks gestation. Women consented to vaginal swabs at each of their visits to the clinic. The dataset contains GC-IMS VOC data from a G.A.S. GC-IMS and includes a Spreadsheet of demographics.</p>
Figure 2 in Bornean caterpillar (Lepidoptera) constructs cocoon from Vatica rassak (Dipterocarpaceae) resin containing multiple deterrent compounds
Figure 2. Pieces of resin taken from the cocoon and imaged (A) using photomontage; and (B– D) environmental electron microscopy. Images (B–D) show the elaborate shearing patterns within the resin. The centre of image (D) shows what may be a score mark in the surface of the resin made by the caterpillar.
Compound Database for eQuilibrator
<p>An indexed database of compounds from the MetaNetX, reference database version 3.1.</p> <p>Structures are enriched with information using OpenBabel and ChemAxon.</p> <p> </p>
PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports
<p><strong>Intro:</strong></p><p>The PharmaCoNER corpus (divided into train, dev and test) is a Gold Standard manually annotated dataset used for the the PharmaCoNER shared task posed at BIONLP-ST (at EMNLP). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations.</p><p>The PharmaCoNER corpus consists of:</p><ul><li>Manually classified clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC).</li><li>It was manually selected by a practicing oncologist and revised by a clinical documentalist to assure that records were relevant/representative and resembled structure and content relevant to process clinical records.</li><li>The final corpus: 1000 clinical cases 16,504 sentences</li><li>The corpus contains a total of 396,988 words, with an average of 396.2 words per clinical case.</li><li>It covers a range of medical disciplines including oncology, urology, cardiology, pneumology or infections diseases, etc.</li><li>The corpus has been annotated at the mention level by experts in medicinal chemistry and pharmacology following a granular annotation scheme covering four mention types:<ul><li><i>Entity type 1 (NORMALIZABLES)</i>: mentions of chemicals that can be manually normalized to a unique concept identifier (primarily SNOMED-CT).</li><li><i>Entity type 2 (NO_NORMALIZABLES)</i>: mentions of chemicals that could not be normalized manually to a unique concept identifier.</li><li><i>Entity type 3 (PROTEINAS)</i>: mentions of proteins/genes following an adaptation of the BioCreative GPRO track annotation guidelines (includes peptides, peptide hormones & antibodies).</li><li><i>Entity type 4 (UNCLEAR )</i>: cases of general substance class mentions of clinical relevance, including certain pharmaceutical formulations, general treatments, chemotherapy programs, and vaccines.</li><li>Mentions class "<i>UNCLEAR</i>" (not evaluated for the PharmaCoNER track)</li></ul></li></ul><p> </p><p> </p><p><strong>Please, cite: </strong></p><p>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</p><p> </p><p><strong>Annotation quality</strong></p><p>Inter-annotator agreement: 93% for annotation, 73% for mapping.</p><p>For more information, see the <a href="https://paperswithcode.com/paper/pharmaconer-pharmacological-substances">paper</a>.</p><p> </p><p><strong>Format</strong></p><p>For subtask 1 annotations are distributed in <i>Brat</i> format. (More info at Brat webpage https://brat.nlplab.org/standoff.html)</p><p>For subtask-2, codes are associated with each document are given in a <i>TSV</i> file with the following columns: </p><blockquote><p>filename code</p></blockquote><p> </p><p><strong>Shared task goal:</strong></p><p>In the two subtasks, the goal is to predict the annotations of the test files (either the ANN files or the TSV with the codes) given only the plain text files. </p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/pharmaconer/"><strong>Web</strong></a></li><li><a href="https://www.aclweb.org/anthology/D19-5701.pdf"><strong>Citation</strong></a><strong>: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li><li><a href="https://doi.org/10.5281/zenodo.4271908"><strong>Silver Standard corpus</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li><li><a href="https://www.youtube.com/watch?v=B3ZzJl5OMkY"><strong>Youtube video(general setting)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/pharmaconer-pharmacological-substances-compounds-and-proteins-named-entity-recognition-track-at-bionlpost-workshop-november-4-skycity-rm-2-hong-kong-emnlp2019"><strong>Slides PharmacoNER overview talk at BIONLP-ST / EMNLP </strong></a></li></ul><p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p><p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p> </p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in PharmaCoNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul>
Non-target screening of organic compounds in offshore produced water by GC×GC-MS (associated data)
<p>Associated data for the manuscript titled "<em>Non-target screening of organic compounds in offshore produced water by GC×GC-MS</em>"</p> <p>Preprint doi://10.26434/chemrxiv.13317938</p> <p> </p>
Bioactive compounds with no structural analogs (high-confidence activity data)
<p>A set of 52,815 unique bioactive compounds (human targets, high-confidence activity data) with no structural analogs with high-confidence activity data was extracted from ChEMBL. For each compound the ChEMBL compound ID (CHEMBLID_Compound) and high-confidence target annotation(s) (CHEMBLID_Targets) are provided. The data set was generated as a part of an analysis to be published in 'Medicinal Chemistry Communications'. </p>
Systematic Design of Analogs of Active Compounds Covering More than 1000 Targets
<p>The analog database consisting of 1,297,204 virtual compounds is provided. Virtual compounds are reported in SMILES representation. In addition, for each virtual compound all available ChEMBL analogs (CHEMBL_COMPOUND_ID) and their activities (CHEMBL_TARGET_IDs) are given.</p>
Highly promiscuous compounds from PubChem assays
<p>For the pool of 466 detected highly promiscuous compounds the PubChem ID and the corresponding ChEMBL ID(s) are provided. In addition, the detection status is set to "pains" or "aggregator" if the compound was detected as PAINS or an aggregator, respectively. Otherwise the status is set "passed". "ChEMBL analogues" lists the ChEMBL compound IDs of structural analogs of highly promiscuous compounds (if available). For the 466 compounds the number of targets and the corresponding PubChem target IDs are given in the last two columns. Compounds 1-26 (Compound No. 1-26) correspond to compounds shown in the publication.</p>
286 new target pairs based on shared compounds from ChEMBL
<p>Reported is the list of 286 compound-based target pairs between distantly related or unrelated pharmaceutical target proteins with shared common compounds, derived from ChEMBL22 high-confidence data. For each target, the corresponding UniProt ID is provided. In addition, for each given target pair, the number of shared compounds and structures by SMILES notations are added as well.</p>
Machine learning and multi-layer molecular network-assisted screening uncovers unknown compounds in the fentanyl family
<p>These LC-HRMS data was collected in study of Fentanyl-Hunte. All source codes along with a user manual are available for scientific research purposes at https://github.com/FangLabNTU/Fentanyl-Hunter.</p>
Compound Flood Risk Guidelines test data
<p>Time series data of flood drivers (discharge, rainfall, coastal water levels) for various case studies in support of compound fllood risk guidelines.</p> <p>version 1: data for Charleston, NC, USA & BrisBane, AUS.</p> <p>version 2; added data for Toamasina, MDG</p> <p>version 3: consistent file structure</p> <p>version 4: reduce coastal time series to 30min temporal resolution</p>
Repo for Compound Continental Risk of Multiple Extreme Floods in the United States
Open the record for dataset details and reuse information.
Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals
<p>We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. </p><p>airqual.qc.csv includes assimilated and verified surface NO2 observations.</p><p>HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.</p><p>posterior_emission_27km.nc and posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.</p>
Data and code for "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass"
<p>This repository provides the data and code for the paper "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass". Almost all data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch</p> <p>In Version 2, an error has been corrected in the computation of the grid cell area, which significantly affected values in Fig. A1.</p>
Data archive for: Resting cells of Skeletonema marinoi assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions
<p>Data archive for: “Resting cells of <em>Skeletonema marinoi</em> assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions” <a href="https://doi.org/10.1111/1462-2920.16625">https://doi.org/10.1111/1462-2920.16625</a></p> <p> </p> <p>Dataset of single cell assimilation of organic/inorganic C/N by resting cells of the marine diatom <em>Skeletonema marinoi</em> captured using secondary ion mass spectrometry (SIMS) and stable isotopic tracers. The dataset also contains POC/PON changes over time during dormancy, DNRA (<sup>15</sup>N-NH<sub>4</sub><sup>+</sup> production), denitrification (<sup>15</sup>N-N<sub>2</sub> production) and a germination assay to determine survival rate, most probable number analysis (MPN). </p> <p>Two strains (GF04 and R05) were incubated in dark and anoxic conditions in two different incubation experiments.</p> <p>Incubation 1: Diatoms treated with antibiotics before entering dormancy compared to a control not treated with antibiotics then given <sup>15</sup>N-NO<sub>3</sub><sup>-</sup> in dark anoxic conditions.</p> <p>Incubation 2: Diatoms treated with antibiotics given, <sup>15</sup>N & <sup>13</sup>C urea, <sup>15</sup>N & <sup>13</sup>C urea + <sup>14</sup>N-NO<sub>3</sub><sup>-</sup>, <sup>13</sup>C-acetate, <sup>13</sup>C-acetate + <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>, or <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>.</p> <p>See the main manuscript for a extensive experimental setup.</p> <p> </p> <p><strong>Each file is uploaded as both a .CSV and .XLSX, so that you can choose which you prefer.</strong></p> <p><strong>DNRA_and_denitrification.csv/xlsx:</strong> DRNA and denitrification depending on volume (Incubation 1)</p> <p><strong>DNRA_per_cell.csv/xlsx:</strong> DNRA per cell (Incubation 1 & 2)</p> <p><strong>MPN_data.csv/xlsx:</strong> Most probable number analysis (Incubation 1 & 2)</p> <p><strong>POC_PON.csv/xlsx:</strong> POC and PON per cell and volume (Incubation 1 & 2)</p> <p><strong>SIMS_data.csv/xlsx:</strong> SIMS data (Incubation 1 & 2)</p> <p> </p> <p> </p>
Dataset for: On the Nature of Hydrophobic Organic Compound Adsorption to Smectite Minerals Using the Example of Hexachlorobenzene-Montmorillonite Interactions
<p><br>This dataset contains all data obtained from first principle DFT calculations at the PBE-D3 DFT level<br>by the program VASP for the paper published in the journal "Minerals". Please cite this article when using the dataset.<br><br>Title: "On the Nature of Hydrophobic Organic Compound Adsorption<br>to Smectite Minerals Using the Example of Hexachlorobenzene-Montmorillonite Interactions."</p> <p>Authors: Peter Grancic, Leonard Böhm, Martin H. Gerzabek, and Daniel Tunega <br>DOI10.3390/min13020280. </p> <p><br>The model systems are Ca-montmorillonite (Ca-Mt) models with varying layer charge from the Mg/Al substitutions.<br>Calculated are interaction energies of hexachlorobenzene (HCB) with Ca-Mt models in various configurations.<br>QE.dat file is file with collected energies of all models, Collect_QE.py is a selfmade python file for the collection of energies.<br>The structure of all directories is as following:<br>HCBCaxxx directories contain optimized HCB-CaMt complexes<br>HCBCaxxx_Clay directories contain pure CaMt models<br>HCBCaxxx_HOC directories contain only HOC molecule<br>Each directory contains:<br>-input geometry data (POSCAR file)<br>-optimized geometries (CONTCAR.norm file)<br>-input parameters for VASP (INCAR file)<br>-k-points (KPOINTS file)<br>-complete output files (OUTCAR.norm and vasprun.xml.norm files)<br>POTCAR file with pseudopotentials are not provided due to copyright rights.<br>Their type can be found in OUTCAR or vasprun.xml files.</p> <p>Funding: This work has been supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), grant number 443637168, BO5388/1–1 and Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung, FWF), grant number I 4876–N in the bilateral project ”Clay minerals as sorbents for hydrophobic organic chemicals – ClayHOC”. The results<br>presented have been achieved using the Vienna Scientific Cluster (VSC), project number 70544.</p> <p>Terms of use: These data are provided "as is", without any warranty. This dataset is provided under the Creative Commons Attribution 4.0 International license.</p>
Text-fig. 9. Miscellaneous leaves. a: Leaf of Anacardiaceae, UAPC-ALTA S 59513. b: Sapindaceous leaf, BBM-PAL-P000046. c: Twig with compound leaves of Averrhoites affinis, UAPC-ALTA S 67694. d, e: cf. Morus. Finely serrate leaf with actinodromous venation, prominent agrophic veins and strongly percurrent tertiary veins, UAPC-ALTA S 67695. f: Leaf with strongly apically arched upper pairs of secondary veins and entire margins, UAPC-ALTA S 59504. g: Compound leaf, UAPC-ALTA S 59504A. h: Detail of (g) showing one leaflet. i: Incomplete basal part of a lamina with rounded base and entire margin, UAPC-ALTA S 6565. j: Compound leaf with leaflets sessile on a stout rachis, UAPC-ALTA sn. Scale bars: a, c, d, i = 2 cm, b, e–h, j = 1 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 9. Miscellaneous leaves. a: Leaf of Anacardiaceae, UAPC-ALTA S 59513. b: Sapindaceous leaf, BBM-PAL-P000046. c: Twig with compound leaves of Averrhoites affinis, UAPC-ALTA S 67694. d, e: cf. Morus. Finely serrate leaf with actinodromous venation, prominent agrophic veins and strongly percurrent tertiary veins, UAPC-ALTA S 67695. f: Leaf with strongly apically arched upper pairs of secondary veins and entire margins, UAPC-ALTA S 59504. g: Compound leaf, UAPC-ALTA S 59504A. h: Detail of (g) showing one leaflet. i: Incomplete basal part of a lamina with rounded base and entire margin, UAPC-ALTA S 6565. j: Compound leaf with leaflets sessile on a stout rachis, UAPC-ALTA sn. Scale bars: a, c, d, i = 2 cm, b, e–h, j = 1 cm.
Text-fig. 6. Betulaceae, Fagaceae, Anacardiaceae. a: Corylites sp., UAPC-ALTA sn. b: Alnus sp., BBM-PAL-sn. c: Fagopsis infructescence, GSC 7586. d: Palaeocarpinus barksdalae, UAPC-ALTA S 59493. e: Fagopsis undulata leaf, UAPC-ALTA S 67688. f: cf. Schinus compound leaf with serrated leaflets basally fused to the rachis, UAPC-ALTA S 24998. g: Rhus sp., UAPC-ALTA S 67689. Scale bars: a, e = 1 cm, b, f = 2 cm, c, d = 0.3 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 6. Betulaceae, Fagaceae, Anacardiaceae. a: Corylites sp., UAPC-ALTA sn. b: Alnus sp., BBM-PAL-sn. c: Fagopsis infructescence, GSC 7586. d: Palaeocarpinus barksdalae, UAPC-ALTA S 59493. e: Fagopsis undulata leaf, UAPC-ALTA S 67688. f: cf. Schinus compound leaf with serrated leaflets basally fused to the rachis, UAPC-ALTA S 24998. g: Rhus sp., UAPC-ALTA S 67689. Scale bars: a, e = 1 cm, b, f = 2 cm, c, d = 0.3 cm.
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.