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264 results for “batch”
Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables
<p>Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain.<br> Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach.<br> Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility.<br> In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data.</p> <p>This repository contains the observed and simulated data that was used in the description paper.</p> <p> </p>
Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors
<p>Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.</p>
Chemical Process Technologies: Batch Reactors
<p>An overview of the fundamentals of batch technology in chemical processing.</p>
Multiple-batch spawning: a risk spreading strategy disarmed by highly intensive size-selective fishing
Open the record for dataset details and reuse information.
Multiple-batch spawning as a bet-hedging strategy in highly stochastic environments: an exploratory analysis of Atlantic cod
Open the record for dataset details and reuse information.
A snakemake toolkit for the batch assembly, annotation, and phylogenetic analysis of mitochondrial genomes and ribosomal genes from genome skims of museum collections
Open the record for dataset details and reuse information.
Supporting material for: A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study
<p>Underlying data, R scripts and subsequent figures are presented to deliver a replicable and transparent analysis for the manuscript "A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study"</p> <p>Published in Scientific Reports</p> <p>See: <a href="https://protect-de.mimecast.com/s/kCQLCgpRGySlzOwmCNma6d?domain=rdcu.be">https://rdcu.be/b3vj2</a> </p> <p> </p> <p>This is version 2 of this data. This differs from version 1 in the following</p> <p>1. The number of simulation has increased from 300 to 2000 in each simulation cycle</p> <p>2. Script has been added to generate publication level figures</p> <p> </p> <p> </p> <p> </p>
OAM spectra - batch 2
<p>In this dataset are collected some of the experimental OAM spectra acquired in a second session of experiments.</p> <p>The collection was carried out on the K2 camera (GIF camera broke) of the Titan Holo @ERC-FZJ </p>
DIC performance for huge strains: The X-Strain DIC Challenge (GeM/ECN batch images)
<p>There are in this repository <strong>2 sets of images </strong>of flat elastomeric membranes that are severely deformed (up to several hundred % deformation). A speckle and dots have been deposited on the membranes in order to perform image analysis and measure the deformation fields. The document ’<em>Large_strain_images.pdf’</em> explains how the images were obtained. These images can be used as a benchmark for digital image correlation codes in case of very large deformations (i.e. The X-Strain DIC Challenge, <a href="http://sem.org/dicchallenge">https://sem.org/dicchallenge</a>).</p> <ul> <li>First set of images : 360 bmp images of size 38.8 Mo (<a href="https://zenodo.org/api/files/dbac452a-e701-4b5d-b05c-edafa7e442c8/20190926.zip">20190926.zip</a>)</li> <li>Second set of images : 820 bmp images of size 32.5 Mo (<a href="https://zenodo.org/api/files/dbac452a-e701-4b5d-b05c-edafa7e442c8/20191220.zip">20191220.zip</a>) </li> <li>Technical data sheet of the materials (<a href="https://zenodo.org/api/files/dbac452a-e701-4b5d-b05c-edafa7e442c8/Data_sheet.zip">Data_sheet.zip</a>)</li> <li>A technical report on how the images where obtained (<a href="https://zenodo.org/api/files/dbac452a-e701-4b5d-b05c-edafa7e442c8/Large_strain_Images.pdf">Large_strain_Images.pdf</a>)</li> </ul>
Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2
<p>EN:</p> <p>This upload contains photographs taken by Eva van der Heijden and Gabriela Montejo-Kovacevich in the Butterfly Genetics Group at the University of Cambridge from the October 2019 (Eva, 18N0602-20N0611, batch1) and September 2020 (GMK, 19N1590- 20N0610, batch2). These two batches might have slightly different zoom/light settings. Batch1 only has JPGs, batch2 has JPGs and raw images (CR2)</p> <p>This batch contains Patricio Salazar's and Nicola Nadeau's specimen collection from Ikiam University. The wild/reared specimens are from mostly across the H. melpomene malleti and H. erato lativitta in the Eastern slope of the Andes collected in 2018-2020.<br> <br> They have a white reflectance standard for calibration. Information on the individual IDs can additionally be found in the csv.</p> <p>Nomenclature</p> <p>19NXXXXXX : unit ID corresponding to individual samples<br> _v _d: ventral or dorsal<br> </p> <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/#ViewID=ContentPage_DetailView&ObjectKey=843e8ec1-41a6-4706-9622-f643132da859&ObjectClassName=EarthCape.Module.Core.ContentPage&mode=View">here</a> for the database, and <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here</a> for FAQ)</p> <p>Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) or Nicola Nadeau (n.nadeau[at]sheffield.ac.uk) for requests.</p> <p> </p> <p>------------------------------------------------------</p> <p>ES:</p> <p>Este repositorio contiene fotografías tomadas en el Butterfly Genetics Group de la Universidad de Cambridge</p> <p>Este lote contiene la colección de especímenes silvestres y criados colectados por Patricio Salazar en 2018/2019 para cria en Ikiam (Ecuador). Los especímenes silvestres son de H. melpomene malleti y H. erato lativitta en la vertiente oriental de los Andes. </p> <p>Nomenclatura</p> <p>CAMXXXXXX: ID de unidad correspondiente a muestras individuales<br> _v _d: ventral o dorsal<br> </p> <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/#ViewID=ContentPage_DetailView&ObjectKey=843e8ec1-41a6-4706-9622-f643132da859&ObjectClassName=EarthCape.Module.Core.ContentPage&mode=View">aquí</a> para la base de datos, y <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí</a> para preguntas frecuentes)</p> <p>Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) o Nicola Nadeau (n.nadeau[at]sheffield.ac.uk) con sus preguntas o peticiones.</p>
Cambridge butterfly wing collection batch 10
<p>Cambridge butterfly wing collection batch 10</p> <p>EN:</p> <p>This upload contains photographs taken by Eva van der Heijden and Gabriela Montejo-Kovacevich in the Butterfly Genetics Group at the University of Cambridge in October 2019 (Eva) until the May 2019 (Gabriela). These belong to four different photo bundles:</p> <p>1. Nicola Nadeau's collection request of samples sequenced with BGI (#4) for GMK altitude project (taken in October 2019)</p> <p>2. Rachel Blow's Ecuador collection 2018 melpomene and timareta, near ikiam (taken in October 2019)</p> <p>3. Panama collected individuals during Cambridge Zoology tropical fieldcourse in August 2018 (taken in October 2019)</p> <p>4. Nicola Nadeau's collection second request of samples sequenced with BGI (#4) for GMK altitude project (taken in May 2019)</p> <p> </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 y Imogen Gavins en el Butterfly Genetics Group de la Universidad de Cambridge desde el 10 de octubre de 2017 hasta el 10 de marzo de 2019.</p> <p>Rango de individuos:</p> <p>CAM040704-CAM041352</p> <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>
Data from: Batch effects in a multi-year sequencing study: false biological trends due to changes in read lengths
High-throughput sequencing is a powerful tool, but suffers biases and errors that must be accounted for to prevent false biological conclusions. Such errors include batch effects, technical errors only present in subsets of data due to procedural changes within a study. If overlooked and multiple batches of data are combined, spurious biological signals can arise, particularly if batches of data are correlated with biological variables. Batch effects can be minimized through randomisation of sample groups across batches. However, in long-term or multi-year studies where data are added incrementally, full randomisation is impossible and batch effects may be a common feature. Here we present a case study where false signals of selection were detected due to a batch effect in a multi-year study of Alpine ibex (Capra ibex). The batch effect arose because sequencing read length changed over the course of the project and populations were added incrementally to the study, resulting in non-random distributions of populations across read lengths. The differences in read length caused small misalignments in a subset of the data, leading to false variant alleles and thus false SNPs. Pronounced allele frequency differences between populations arose at these SNPs because of the correlation between read length and population. This created highly statistically significant, but biologically spurious, signals of selection and false associations between allele frequencies and the environment. We highlight the risk of batch effects and discuss strategies to reduce the impacts of batch effects in multi-year high-throughput sequencing studies.
Data from: Batch spawning facilitates transfer of an essential nutrient from diet to eggs in a marine fish
Fatty acid composition of eggs affects development, growth, and ecological performance of fish embryos and larvae, with potential consequences for recruitment success. Essential fatty acids in eggs derive from the maternal diet, and the time between ingestion and deposition in eggs is ecologically important but unknown. We examined the dynamics of diet-egg transfer of arachidonic acid (ARA) in the batch-spawning fish, red drum (Sciaenops ocellatus), by measuring ARA concentrations in eggs after a single diet shift and during a period of irregular variations in diet. ARA concentrations in eggs changed within 2-16 days of a diet shift. The rate of change was proportional to the magnitude of the shift, with no evidence of equilibration. These results are not consistent with eggs being assembled entirely from accumulated body stores. The immediate source of ARA in eggs appears to be the recent diet. We propose that batch spawning produces rapid diet-egg transfer of ARA because it removes large amounts of fatty acids from the body and prevents equilibration. The immediacy of the diet-egg connection suggests that spawning migration combined with short-interval batch spawning may have evolved to take advantage of nutrients critical for offspring survival that are available at the spawning site.
A Novel Flow Shop Batch Scheduling Model for Subtractive Processing in Electronic Product Manufacturing
<p>The data is randomly generated based on real manufacturing enviroment. The data is used to evaluate the performance of algorithms proposed in our research.</p>
S Figure 1. Maximum Photosynthetically Active Radiation (PAR) during the experiments in the HTP platform, for A) batch 1 (1 July – 5 August 2021; 87 – 122 DAS) and B) batch 2 (7 August – 6 September 2021; 92 – 123 DAS)
<p>The figure is presented in a figure (S Figure 1.<strong> </strong>Maximum Photosynthetically Active Radiation (PAR) during the experiments in the HTP platform, for A) batch 1 (1 July – 5 August 2021; 87 – 122 DAS) and B) batch 2 (7 August – 6 September 2021; 92 – 123 DAS) as supplement of a manuscript in MDPI <i>Agronomy | </i>Special Issue<i> "Novel Insights into Abiotic Stress Tolerance of Crops" </i>journal. </p>
Data for the original research article 'Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor'
<p>All data that support the findings of the study 'Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor' published in GCB Bioenergy Journal are openly available in the file.</p>
Data for: Ascent rates of 3D fractures driven by a finite batch of buoyant fluid
<p>Ascent rates of finite fluid batches:</p> <p>Contains the numerical experiments using https://pyfrac.epfl.ch/</p> <p>Also contains movies and text data from selected analog gelatin experiments of <em>Smittarello, D. 2019 Propagation des intrusions basaltiques. PhD thesis, Université Grenoble Alpes.</em></p> <p>See ReadMe file for more information. </p> <p>Files zipped using 7zip.</p>
bOTTR: Batch Instantiation of OTTR templates
<div> <p>This specification defines <em>bOTTR</em>, a language for specifying mappings between queries over the sources to given templates. bOTTR hence allows multiple data sources on different formats to be integrated via OTTR templates into a single RDF/OWL representation.</p> <p>The vocabulary of the bOTTR language is specified by an OWL ontology which extends the wOTTR ontology.</p> </div>
Dataset - Selection of cyanobacteria over green algae in a photo-sequencing batch bioreactor fed with wastewater
<p>The data set attached consists of an excel file where the data from the article “<strong>Selection of cyanobacteria over green algae in a photo-sequencing batch bioreactor fed with wastewater”</strong>, published in Science of the Total Environment (vol. 653, 25<sup>th</sup> February 2019, 485-495.) can be found, as well as the different equations and formulae that were used to obtain the published results.</p>
Single Batching Machine Problem Instances from AS/RS
<p>The following instances are originally presented in the working paper "Logic-based Benders decomposition for scheduling a batching machine" written by Emde, Simon, Polten, Lukas, and Gendreau, Michel.</p> <p>The data corrospond to 540 instances each in its own file. Each file is structured as follows:</p> <p>The first line contains: the number of jobs, the number of batches and the maximum batch size</p> <p>Then there follows a line for each job containing first the processing time and then the deadline.</p> <p>Then a line with the number of the following precedence constraints.</p> <p>Then the lines for each precedence constraints, where the first job has to precede the second job. Note that the job are numbered starting from 0.</p> <p>Then a line with the number of incompatibilites.</p> <p>Then the lines for each incompatibility, containing two numbers, corresponding to a pair of jobs, that cannot be packed into the same batch.</p> <p>All numbers in the same line are seperated by spaces and all numbers are integers.</p> <p> </p>
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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.