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6,467 results for “process”
Ecological and evolutionary processes shape belowground springtail communities along an elevational gradient
<p> The data sets were analyzed during the current study. Data from: Ecological and evolutionary processes shape belowground springtail communities along an elevational gradient</p>
KROA Processed Data
<p>Time series of model forecast and observation data at KROA during the years: 2011- 2020. </p> <p>The dataset has been provided as part of the <a href="https://www.ametsoc.org/index.cfm/ams/education-careers/careers/professional-development/short-courses1/machine-learning-in-python-for-environmental-science-problems1/">2022 AMS AI Short Course</a>.</p>
Pre-processed raw data of 4D-SCED 4D-STEM(NBD) datasets
<p>The raw 4D-SCED and 4D-STEM (NBD) datasets in our paper:</p> <p>Seeing Structural Evolution of Organic Molecular Nano-crystallites Using 4D Scanning Confocal Electron Diffraction</p> <p>https://arxiv.org/abs/2110.02373v1</p> <ul> <li>Dose-damage_RT_and_Cryo_DRCN5T_PCBM.zip<br> - EF-Diff_cryo_0.16e_per_A2_per_frame.dm4<br> - EF-Diff_RT_0.14e_per_A2s_per_frame.dm4</li> <li>Fig_2_SCED-vs-NB_DH6T.zip<br> - 00.43.44 Spectrum image_1.dm4: raw 3D diffraction pattern stack (NBD) of size 512*512*2500, only converted file format, no other pre-processing applied<br> - 00.54.07 Spectrum image_1.dm4: raw 3D diffraction pattern stack (SCED) of size 512*512*2500, only converted file format, no other pre-processing applied<br> - STEM HAADF 0103.emd: Velox raw image of the region of interest acquired at the end of experiment.<br> - STEM HAADF 0103 data.jpg: exported JPG image with databar from the above raw image file</li> <li>Fig_3n4_4D-SCED-vs-4D-NBD_DRCN5T_PCBM.zip<br> - 1201 4D STEM Dataset SCED.dm4: pre-processed 4D-SCED dataset<br> - 1209 4D STEM Dataset NBD.dm4: pre-processed 4D-STEM (NBD) dataset</li> <li>Fig_5_insitu_4D-SCED_datasets.zip<br> - 1431 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ RT<br> - 1431 SI HAADF RT.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1431 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1455 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 100C<br> - 1455 SI HAADF 100C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1455 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1505 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 120C<br> - 1505 SI HAADF 120C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1505 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1516 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 140C<br> - 1516 SI HAADF 140C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1516 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1529 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 160C<br> - 1529 SI HAADF 160C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1529 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file</li> <li>STEM-EELS_DRCN5T_PCBM_SVA_CS.zip<br> - ADF Image (SI survey).dm4: simultaneously acquired STEM-ADF image<br> - EELS Spectrum Image (high-loss).dm4: high-loss part of the DualEELS STEM-EELS dataset<br> - EELS Spectrum Image (low-loss).dm4: low-loss part of the DualEELS STEM-EELS dataset</li> <li>STEM-EELS_DRCN5T_PCBM_SVA_CHCl3.zip<br> - ADF Image (SI survey).dm4: simultaneously acquired STEM-ADF image<br> - EELS Spectrum Image (high-loss).dm4: high-loss part of the DualEELS STEM-EELS dataset<br> - EELS Spectrum Image (low-loss).dm4: low-loss part of the DualEELS STEM-EELS datase</li> </ul> <p> </p>
Utilizing Hydrothermal Processing to Align Structure and In Vitro Digestion Kinetics between Three Different Pulse Types
<p>The data used for the graphs in the paper: Pälchen, K.; Van den Wouwer, B.; Duijsens, D.; Hendrickx, M.E.; Van Loey, A.; Grauwet, T. Utilizing Hydrothermal Processing to Align Structure and In Vitro Digestion Kinetics between Three Different Pulse Types. <em>Foods </em><strong>2021</strong>, <em>11</em>, 206. https://doi.org/10.3390/foods11020206</p> <p><strong>Abstract</strong></p> <p>Processing results in the transformation of pulses’ structural architecture. Consequently, digestion is anticipated to emerge from the combined effect of intrinsic (matrix-dependent) and extrinsic (processed-induced) factors. In this work, we aimed to investigate the interrelated effect of intrinsic and extrinsic factors on pulses’ structural architecture and resulting digestive consequences. Three commercially relevant pulses (chickpea, pea, black bean) were selected based on reported differences in macronutrient and cell wall composition. Starch and protein digestion kinetics of hydrothermally processed whole pulses were assessed along with microstructural and physicochemical characteristics and compared to the digestion behavior of individual cotyledon cells isolated thereof. Despite different rates of hardness decay upon hydrothermal processing, the pulses reached similar residual hardness values (40 N). Aligning the pulses at the level of this macrostructural property translated into similar microstructural characteristics after mechanical disintegration (isolated cotyledon cells) with comparable yields of cotyledon cells for all pulses (41–62%). We observed that processing to equivalent microstructural properties resulted in similar starch and protein digestion kinetics, regardless of the pulse type and (prolonged) processing times. This demonstrated the capacity of (residual) hardness as a food structuring parameter in pulses. Furthermore, we illustrated that the digestive behavior of isolated cotyledon cells was representative of the digestion behavior of corresponding whole pulses, opening up perspectives for the incorporation of complete hydrothermally processed pulses as food ingredients.</p>
Selective laser melting process of tool steel powder
<p>The video demonstrates one of the most commercialised powder bed fusion techniques of additive manufacturing, the Selective Laser Melting (SLM). Tool steel powder (Gas atomized ASP2023®) is processed using a a commercial SLM instrument (AconityMINI). During the SLM process, the powder is delivered to the pre-heated powder bed through the powder delivery system and then selectively melted using the laser beam in a chamber filled with argon (see attached figure about the chamber and the key parts of the process). The delivered powder particles are solidified to form layers, as the process continues until the desired three-dimensional component is fabricated. </p>
Processed data for KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"
<p>This repository contains processed data associated with the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al <em>bioRxiv</em> 2021).</p> <p>In this study, single-cell lineage tracing was performed in the <em>KP</em> autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with <em>Cre</em> recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above. </p> <p>Data here represents the derived processed data from our analysis. The specific contents are described in a README contained within the repository.</p> <p>Briefly, however, we have provided here processed AnnData objects for the KP data (sgNT) and the integrated data across three genotypes (KP, KPA, and KPL). We also provide the gene lists associated with fitness, expansion annotations for each tree, as well as plasticity scores amongst other items. </p> <p>For code used in this study, we additionally have provided a reproducibility repository at https://github.com/mattjones315/KPTracer-release. </p>
NMR assignment of methyl groups in solid-state using 1H-detection and fast MAS - NMR raw and processed data
<p>This data set contains raw NMR data in Bruker format for experimental series on (1) 2,3-13C-labelled microcrystalline alanine, (2) U-13C,15N-labelled N-fomylated microcrystalline tripeptide Met-Leu-Phe, and (3,4) two differently labelled (ILV-C4 and ILV-C5) microcrystalline chicken-alpha-spectrin SH3 domain. Measurements were performed at 14.4 T and 55.5 kHz MAS (alanine), 18.8 T and 55.5 and 98 kHz MAS (fMLF), 23.5 T and 55.5 kHz (SH3 C5), 18.8T and 55.5 and 94.5 kHz MAS (SH3 C5) and 18.8T and 55.5 and 94.5 kHz MAS (SH3 C4). The data set also contains Fourier processed data (spectra) in UCSF format, Sparky project, save and peak list files. Pulse programs for Bruker spectrometers are provided. The data set is complemented with SIMPSON scripts for simulation of spin dynamics under aformentioned conditions.</p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
Adiabatic and diabatic signatures of ocean temperature variability - ACCESS-CM2 processing/plotting code and processed data
<p>Contains processed data and processing/plotting code for the figures in the article:</p> <p>Holmes, R.M., Sohail, T. and Zika, J.D. (2022): Adiabatic and diabatic signatures of ocean temperature variability, Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-21-0695.1">https://doi.org/10.1175/JCLI-D-21-0695.1</a></p> <p>For more information please see the published article, as well as the github repository where the code is described in more detail: <a href="https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695">https://github.com/rmholmes/CM2_HCvar/tree/JCLI-D-21-0695</a></p>
A case study of the dimensional effects in an additive manufacturing process with multiple operations
<p>This dataset provides a case study for the dimension changes of the prints in an additive manufacturing process with multiple operations. The operations are Stereolithography (SLA) printing and its two post-processing processes (washing and post-curing). The dataset contains the measured dimensions of the prints after each operation, which allows the modeling of each operation individually.<br> </p>
Process Performance Indicators for IT Service Management: The PPI Dataset
<p>Set of process performance indicators defined in natural language, related to the IT service management processes of several public organizations in Spain as published in <em>Estrada-Torres, B., del-Río-Ortega, A., Resinas, M., & Ruiz-Cortés, A. (2021). Process Performance Indicators for IT Service Management: The PPI Dataset. In Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts (pp. 125-132). CRC Press. DOI: <a href="https://doi.org/10.1201/9781003168393-8">https://doi.org/10.1201/9781003168393-8</a></em></p>
Dataset and processing scripts for manuscript titled "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors"
<p>The manuscript "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors" contains results for high-efficiency, low-polarization-sensitivity superconducting microwire single-photon detectors. All of the data gathered and the python scripts used for processing is being provided as a zip archive, along with a digital signature file. The dataset contains crucial procedures regarding the equipment calibrations used for accurate device efficiency measurements.</p>
Processed FDG-PET data from: A computational model of neurodegeneration in Alzheimer's disease
<p>Disruption of mental functions in Alzheimer's disease (AD) and related disorders is accompanied by selective degeneration of brain regions. These regions comprise large-scale ensembles of cells organized into systems for mental functioning, however the relationship between clinical symptoms of dementia, patterns of neurodegeneration, and functional systems is not clear. We developed a model of the association between dementia symptoms and degenerative brain anatomy using F18-fluorodeoxyglucose (FDG) PET and dimensionality reduction techniques patients with AD. This data and code package contains preprocessed FDG-PET images from 423 subjects across the Alzheimer's disease spectrum and the MATLAB code to produce eigenbrains from this data.</p>
Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes
<p>Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories ( i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.</p>
Opponent processing in the retinal mosaic of nymphalid butterflies
<p class="MsoBodyText">The eyes of nymphalid butterflies, investigated with incident illumination, show colourful facet reflection patterns, the eye shine, which is uniform or heterogeneous, dependent on the species. Facet colours suggest that the ommatidia contain different sets of photoreceptors and screening pigments, but how the colours and the cell characteristics are associated has not been clearly established. Here we analyse the retinae of two nymphalids, <em>Apatura ilia</em>, which has a uniform eyeshine, and <em>Charaxes jasius, </em>a species with a heterogeneous eye shine, using single-cell recordings, spectroscopy and optical pupillometry. <em>Apatura</em> has UV, blue and green-sensitive photoreceptors, allocated into three ommatidial types. The UV and blue-sensitive cells are long visual fibres (LVFs), receiving opponent input from the green-sensitive short visual fibres (SVFs). <em>Charaxes</em> has an expanded set of photoreceptors, allocated into three additional, red-reflecting ommatidial types. All red ommatidia contain green-sensitive LVFs, receiving opponent input from red receptors. In both species, the SVFs do not receive any opponent input. The simple retina of <em>Apatura</em> with three ommatidial types and two different colour-opponent channels can support trichromatic vision. <em>Charaxes</em> has six ommatidial types and three different colour-opponent channels. Its expanded receptor set can support tetrachromatic vision.</p>
Processed capture histories
<p>Data from PTAGIS.com processed into capture histories for Wenatchee spring Chinook marked at screw traps in the Chiwawa, Nason, and White Rivers. For use in an analays in from the githgub repo marksorel8/Wenatchee-survival</p>
Processed model output and observational products used in `Observed winds crucial for September Arctic sea ice loss'
<p>Processed model output from wind-nudging experiments used to investigate Arctic sea ice loss. Also includes processed observational data shown in the manuscript.</p> <p> </p> <p>For further details, see </p> <p>Roach, L. A and Blanchard-Wrigglesworth E. (2022). Observed winds crucial for September Arctic sea ice loss. Accepted at Geophysical Research Letters</p>
Fairlex: A multilingual benchmark for evaluating fairness in legal text processing
<p>We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.</p>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Pathogen reduction data when applying high pressure processing to milk/colostrum and ready-to-eat foods
<p><strong>Introduction</strong></p> <p>This dataset was built by members of the EFSA Working Group on high pressure processing (HPP) of food during the preparatory work on the BIOHAZ Scientific Opinion on the efficacy and safety of high pressure processing of food (EFSA-Q-2020-00380) (<a href="https://doi.org/10.2903/j.efsa.2022.7128">https://doi.org/10.2903/j.efsa.2022.7128</a>).</p> <p>It was used to answer to the following Terms of Reference (ToR) of the mandate:</p> <p>ToR2. To assess the efficacy of HPP when applied to raw milk and raw colostrum from ruminants, and in particular:</p> <p>a. To recommend minimum requirements as regards time and pressure of the HPP, and other factors if relevant, for the control of <em>Mycobacterium</em> spp., <em>Brucella</em> spp., <em>Listeria monocytogenes</em>, <em>Salmonella</em> spp. and Shiga toxin-producing <em>Escherichia coli</em> (STEC), to achieve an equivalent efficacy to that of thermal pasteurisation.</p> <p> </p> <p>ToR3. To assess the efficacy of HPP when applied to foods known to cause human listeriosis and in particular:</p> <p>a. To recommend minimum requirements as regards time and pressure of the HPP, and other factors if relevant, to reduce significantly <em>L. monocytogenes</em> levels (e.g. by a certain log reduction), and assuming that the parameters influencing the growth of <em>L. monocytogenes</em> remain unchanged (e.g. shelf-life and storage conditions);</p> <p>b. To assess the efficacy on other relevant pathogens when applying the minimum requirements identified in a.</p> <p>A literature search was conducted to retrieve studies reporting on pathogen-specific parameters when treating milk/colostrum and ready-to-eat foods with HPP. A modelling approach was used to fit the derived log<sub>10</sub> reductions (logR) at specific pressure-holding time combinations. More information can be found in the Scientific Opinion.</p> <p><strong>Description</strong></p> <p>Two MS-Excel files contain the extracted data.</p> <p>One file consists of the data used for answering ToR2 and contains data on HPP inactivation of <em>Mycobacterium bovis</em>, <em>L. monocytogenes</em>, <em>Salmonella</em> spp., STEC, <em>Campylobacter</em> spp., and <em>Staphylococcus aureus</em> in milk and colostrum from ruminants.</p> <p>The other file was used for answering ToR3 and contains data on HPP inactivation of <em>L. monocytogenes</em>, <em>Salmonella</em> spp. and <em>E. coli</em> in three types of RTE food categories: category “cooked meat products”, category “smoked and gravad fish” and category “soft or semi-soft and fresh cheese”.</p> <p>Both files contain metadata related to product characteristics (e.g. the food matrix and composition), contamination characteristics (e.g. pathogen/strain used for inoculation, inoculation level, medium used for enumeration), HPP treatment characteristics (e.g. target pressure, time), and outcome (e.g. log<sub>10</sub> reduction).</p>
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.