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264 results for “batch”
PanLex, OntoLex-Lemon edition, Batch 1/8 (dictionaries 0..999)
<p>PanLex (panlex.org) is a large-scale collection of dictionaries. Here, we provide a machine-readable edition in RDF, using the OntoLex-Lemon vocabulary and under resolvable URIs. We achieve resolvability by means of a redirection service, at the moment, purl.org. Feel free to explore this data with http://purl.org/acoli/dicts/panlex/000/0.ttl, for example.</p> <p>For details of the conversion process, see (and refer to):</p> <p>Chiarcos, C., Fäth, C., & Ionov, M. (2020, May). The ACoLi dictionary graph. In <em>Proceedings of The 12th Language Resources and Evaluation Conference</em> (pp. 3281-3290), https://aclanthology.org/2020.lrec-1.401.pdf</p> <p>Note that Zenodo does not allow us to publish this data under an RDF-compliant media type. We therefore deviate from general naming recommendations and include the file type in the URIs. Some SPARQL clients (e.g., Apache Jena) use the file extension to guess the proper media type. Also note that these heuristics will fail if the standard Zenodo flags are attached to files, so please use bare files, only.</p> <p>Licensing follows the original data. See PanLex batch on language metadata.</p>
Sewage sludge batches chemical characterization
<p><a href="https://github.com/jmalonso55/Sewage-sludge-characteristics">https://github.com/jmalonso55/Sewage-sludge-characteristics</a></p> <p>This repository contains the R script and part of the data from Alonso, J.M., Ph.D. Thesis (Alonso, J.M., 2019. Caracterização de Biossólidos para a Produção de Mudas de Espécies Arbóreas da Mata Atlântica [Thesis]. Universidade Federal Rural do Rio de Janeiro, Seropédica, Brasil). </p> <p>Published as a paper with the reference: Alonso, J.M., Abreu, A.H.M., Andreoli, C.V. <em>et al.</em> Chemical characteristics and valuation of sewage sludge from four different wastewater treatment plants. <em>Environ Monit Assess</em> <strong>196</strong>, 34 (2024). <a href="https://doi.org/10.1007/s10661-023-12211-8">https://doi.org/10.1007/s10661-023-12211-8</a></p> <p>The data in this particular repository are from samples of 19 sewage sludge batches. The samples were collected in January, May, August, December 2016, February, June, September, and December 2017 from four wastewater treatment plants (WWTP). At WWTP Ilha do Governador, samples were collected in all eight seasons. Due to various reasons, it was not always possible to collect samples from the other WWTPs. The WWTP Alegria had five collections, while Barra and Sarapuí had three.</p> <p>The chemical characterization of the sludges was conducted based on the parameters required by Resolution nº 498/2020 of the National Council for the Environment (BRASIL, 2020). The elements P, K, Ca, Mg, Fe, Al, Na, Co, Mn, As, Ba, Cd, Cr, Cu, Ni, Se, Pb, and Zn were determined using inductively coupled plasma optical emission spectrometry (ICP-OES). The dry combustion method determined the C and N contents using a CHN-600 elemental autoanalyzer. The organic matter content was determined by multiplying the total carbon present in each sample by the “van Bemmelen” conversion factor of 1.724. The pH and electrical conductivity (EC) were determined by taking 5 g of each sample and diluting it in 50 mL of deionized water. The mixture was stirred for 30 minutes and then measured using a bench pH and conductivity meter.</p> <p>The valorization of the sludge batches was performed using the substitute goods method, which compares the prices of products well-established in the market with the good to be valued. The sludge was valued for its N, P, and K contents. The inputs quoted as sources of these nutrients were the mineral fertilizers urea (N), triple superphosphate (P), and potassium chloride (K). The fertilizers' megagram prices were divided by the percentage of nutrients they contain to calculate each nutrient's value.</p> <p>Fertilizer prices were consulted in: CONAB. Companhia Nacional de Abastecimento. Relatório de Insumos Agropecuários, Grupo Fertilizante, Sub-Grupo Químico, UF PR, Ano 2023. Brasília: CONAB, 2023. Available from: <a href="https://consultaweb.conab.gov.br/consultas/consultaInsumo.do?method=acao">https://consultaweb.conab.gov.br/consultas/consultaInsumo.do?method=acao</a> CarregarConsulta (accessed 26 July 2023).</p>
Dataset for publication: Validation of large-volume batch solar reactors for the treatment of rainwater in field trials in sub-Saharan Africa, Reyneke et al. (2020). DOI: 10.1016/j.scitotenv.2020.137223
<p>Datasets, Supplementary Information and Water Safety Plan (Assessment Form and Risk Matrix) for the publication: "Validation of large-volume batch solar reactors for the treatment of rainwater in field trials in sub-Saharan Africa" which was published in Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2020.137223).</p>
OAM spectra- Batch 1
<p>In this dataset are collected some of the experimental OAM spectra collected using the GIF camera of the Titan Holo microscope at the Ernst-Ruska Centre in Forschungzentrum Julich.</p>
Display a simulation of a batch or fed-batch cultivation of S. cerevisiae
<p>The model is implemented as it is described in the paper "Pham, Larsson, Enfors - 1998 - Growth and energy metabolism in aerobic fed-batch cultures of Saccharomyces cerevisiae Simulation and mod".<br> The batch and fed-batch are both implemented, the oscillation function was not used.<br> All parameters were taken from the paper and are saved under parameters.py. </p>
Train-B dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR). Batch 1 and Batch 2.
<p>Train-B Dataset. Dataset of pages without any layout or text line information. The corresponding transcripts are provided at page level with line breaks. It has 10k pages, though for convenience it is divided into two 5k page batches. This information is provided in PAGE format. </p> <p>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439807#.WOIBZ3WLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p> <p> </p>
MSV000093464_batch_03
<p>.ttl graphs build from the files in positive ionization mode, batch 03/04. </p><p>Input dataset https://doi.org/10.5281/zenodo.10198219</p><p>Original dataset https://doi.org/doi:10.25345/C5SB50</p>
MSV000093464_batch_02
<p>.ttl graphs build from the files in positive ionization mode, batch 02/04. </p><p>Input dataset https://doi.org/10.5281/zenodo.10198219</p><p>Original dataset https://doi.org/doi:10.25345/C5SB50</p>
MSV000093464_batch_04
<p>.ttl graphs build from the files in positive ionization mode, batch 04/04. </p><p>Input dataset https://doi.org/10.5281/zenodo.10198219</p><p>Original dataset https://doi.org/doi:10.25345/C5SB50</p>
MSV000093464_batch_01
<p>.ttl graphs build from the files in positive ionization mode, batch 01/04. </p><p>Input dataset https://doi.org/10.5281/zenodo.10198219</p><p>Original dataset https://doi.org/doi:10.25345/C5SB50</p>
Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs.
BIO4AFRICA_Small-scale HTC batch unit_Dataset1_261124_v1
<p><span>Hydrochar Characterization Database: This database reflects the characterization of hydrochar produced at IHE Delft for Water Education within the framework of the Bio4Africa project. It includes the experimental operational conditions, the types of biomass used, the mass yield of the product, and the properties of the solid and liquid outputs of the process.</span></p> <p><span><span>Machine learning database: This is an analysis ready database to be used by user who are interested in predicting the properties of hydrochar using machine learning approaches. The database combines the experimental results from IHE and the experimental results from the literature. The data are standardized in the same manner, allowing the users to directly use it without any required edits.</span></span></p>
MetaPro: a web-based metabolomics application for MS data batch inspection and library curation
<p>MetaPro is a metabolomics web analysis platform built on the Aird data format with high performance and high compression. This platform includes a series of necessary functions for metabolomics analysis such as quality control, retention time(RT) alignment, target analysis, untarget analysis, manual integration, batch inspection, MS2 library establishment, and report export, providing efficient data analysis, management and visualization capabilities</p>
Fig. 3 in Egg Batches Parasitism Of Processionary Moth, Thaumetopoea Pityocampa (Lepidoptera, Thaumetopoeidae), From Two Atlas Cedar Ecotypes In Algeria
Fig. 3. Distribution of the number of egg rows in relation to the twig diameter in Chréa (A), and Ouled Yagoub (B).
Fig. 1 in Egg Batches Parasitism Of Processionary Moth, Thaumetopoea Pityocampa (Lepidoptera, Thaumetopoeidae), From Two Atlas Cedar Ecotypes In Algeria
Fig. 1. Eggs batches of Thaumetopoea pityocampa: A — cylindrical form; B — egg batches in thick twigs; C — types of eggs.
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Normalized and batch-corrected concentration of 43 immune markers from 248 subjects with stress-related mental disorders and 36 healthy controls
<h1>Abstract</h1> <p>In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small markers panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.</p> <h1>Methods</h1> <p>The normalized and batch-corrected concentration of 43 immune markers from 237 subjects with stress-related mental disorders and 36 healthy controls was determined in plasma. This data was used in the initial analysis. Additionally, the same measurements are provided for 11 subjects with stress-related mental disorders used in a replication analysis.</p> <p>- blood was collected in the morning under fasted conditions and plasma stored at -80°C until further processing<br>- samples were randomized into 96 well plates<br>- immune marker concentration was measured with the Meso Scale Diagnostics V-PLEX Human Biomarker 54-Plex Kit and the MESO QuickPlex SQ 120 imager according to the manufacturer's instructions<br>- additionally, high-sensitivity C-reactive protein (Tecan Group Ltd.), cortisol (Tecan Group Ltd.), interleukin (IL)-6 (Thermo Fisher Scientific), IL-6 soluble receptor (Thermo Fisher Scientific) and IL-13 (Thermo Fisher Scientific) was measured via ELISA according to the manufacturer's instructions<br>- values below the detection limit in markers measured with ELISA were set to zero and values above the detection limit to the upper limit<br>- the data was quantile-normalized (values were ranked and mapped to the quantiles of a standard normal distribution)<br>- the normalized concentration was corrected for the biobank storage position (batch_variable) via a linear model and the residuals reported as the concentration</p> <p>The following markers were measured:<br>fibroblast growth factor 2 (FGF2 or bFGF), cortisol, C-C motif chemokine 11 (CCL11 or eotaxin), CCL26 (eotaxin-3), vascular endothelial growth factor receptor 1 (VEGFR1 or Flt-1), hsCRP, intercellular adhesion molecule (ICAM)-1, interferon (IFN)-gamma, IL-1alpha, IL-1 receptor antagonist (IL-1RA), IL-10, IL-12/IL-23p40, IL-12p70, IL-13, IL-15, IL-16, IL-17A, IL-17B, IL-2, IL-27, IL-31, IL-5, IL-6 high sensitivity (IL-6HS), IL-7, IL-8HS, C-X-C motif chemokine 10 (CXCL10 or IP-10), CCL2 (MCP-1), CCL13 (MCP-4), CCL22 (MDC), CCL3 (MIP-1alpha), CCL4 (MIP-1beta), placental growth factor (PlGF), serum amyloid A (SAA), sIL-6R, CCL17 (TARC), angiopoietin-1 receptor (Tie-2), tumor necrosis factor (TNF or TNF-alpha), lymphotoxin-alpha (LT-alpha or TNF-beta), thymic stromal lymphopoietin (TSLP), vascular cell adhesion protein 1 (VCAM-1), vascular endothelial growth factor (VEGF)-A-HS, VEGF-C, VEGF-D</p> <p>The data is provided as a tab separated file.</p>
Cambridge butterfly wing collection batch 1
<p>EN:</p> <p>This upload contains photographs taken by Eva Whiltshire in the Butterfly Genetics Group at the University of Cambridge from the 10th October 2017 until the 9th March 2018. </p> <p>Subcollections found in this batch:</p> <ul> <li>Patricio Salazar's wild specimen collection mostly across the H. m. plesseni/malleti and H. e. notabilits/lativitta hybrid zones in the Eastern slope of the Andes collected in 2009 - 2011.</li> </ul> <p>ID range:</p> <p>CAM016006 - CAM017987</p> <p>Nomenclature</p> <ul> <li>CAMXXXXXX : unit ID corresponding to individual samples</li> <li>_v _d: ventral or dorsal</li> </ul> <p>Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click <a href="https://heliconius.ecdb.io/default.aspx">here for the database</a>, and <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here for FAQ</a>)</p> <p>Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk), Gabriela Montejo-Kovacevich (mgm49[at]cam.ac.uk) or Ian Warren (iaw22[at]cam.ac.uk) for requests.</p> <p> </p> <p>------------------------------------------------------</p> <p>ES:</p> <p>Este repositorio contiene fotografías tomadas por Eva Whiltshire en el Butterfly Genetics Group de la Universidad de Cambridge desde el 10 de octubre de 2017 hasta el 9 de marzo de 2018.</p> <p>Subcolecciones encontradas en este lote:</p> <ul> <li>Colecciones de Gabriela Montejo Kovacevich y colegas de Mocoa (Colombia), vertientes oriental y occidental de los Andes en Ecuador (provincias de Napo y Pichincha) de 2017 y 2018.</li> <li>La colección de especímenes silvestres de Patricio Salazar principalmente en la zona híbrida y alrededores de H. m. plesseni / malleti y H. e. notabilits / lativitta en la vertiente oriental de los Andes recogidas en 2009 - 2011.</li> </ul> <p>Nomenclatura</p> <ul> <li>CAMXXXXXX: ID de unidad correspondiente a muestras individuales</li> <li>_v _d: ventral o dorsal</li> </ul> <p>Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (haga clic <a href="https://heliconius.ecdb.io/default.aspx">aquí para la base de datos</a>, y <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí para preguntas frecuentes</a>)</p> <p>Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk), Gabriela Montejo-Kovacevich (mgm49 [arroba] cam.ac.uk) o Ian Warren (iaw22 [arroba] cam.ac.uk) con sus preguntas o peticiones.</p>
geochemical data from UK and US produced water batch experiments
<p>Geochemical data set from batch experiments of UK and US shale gas produced water</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.