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6,467 results for “process”
The process of HDAC11 Assay Development: time-course study follow-up
<p>A follow-up to check the increased fluorescence signals at 0 min of the HDAC11 reaction with its substrate was performed. </p> <p>Version2 (v2) is updated with the reaction recipe.</p>
The process of HDAC11 Assay Development: time-course study
<p>Using Boc-Lys-(TFA)-AMC substrate, a preliminary time-course study was initiated to determine the appropriate HDAC11 concentration and the time to be used for the reaction (which are essential parameters to perform Km calculations later).</p> <p>The updated version2 (v2) has the corrected reaction recipe.</p>
The process of HDAC11 Assay Development: a kit-free method
<p>In order to develop an assay for HDAC11, a kit-free method is being attempted wherein Boc-Lys-(TFA)-AMC, a previously reported substrate was used to check the activity of HDAC11 in a preliminary experiment.</p> <p>In version2 (v2), the reaction recipe components are corrected (a missing component in previous version).</p>
The process of HDAC11 Assay Development: Z'-factor
<p>As a last step, to check if the assay qualifies for the screening purposes, Z’-factor was calculated here.</p>
FIgS. 26–29. Urodacus butleri, n. sp., paratype ♂ (WAM T85141). 26. Dextral hemispermatophore, dorsal aspect. 27–29. Detail of capsule lamellae, dorsal, ental, and ventral aspects. Abbreviations: Al, anterior lobe; C, capsule; Cp, conical process; DTR, distal transverse ridge; L, lamella; Lh, lamellar hook; Lhp, lamellar hook process; IBL, internobasal lobe; T, trunk. Scale bars = 0.5 mm.
FIgS. 26–29. Urodacus butleri, n. sp., paratype ♂ (WAM T85141). 26. Dextral hemispermatophore, dorsal aspect. 27–29. Detail of capsule lamellae, dorsal, ental, and ventral aspects. Abbreviations: Al, anterior lobe; C, capsule; Cp, conical process; DTR, distal transverse ridge; L, lamella; Lh, lamellar hook; Lhp, lamellar hook process; IBL, internobasal lobe; T, trunk. Scale bars = 0.5 mm.
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 1 Questionnaires: Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia – EXTRACT_Political passivity
<p>This dataset contains the data underlying the following publication: Dahl, V., Amnå, E., Banaji, S., Landberg, M., Šerek, J., Ribeiro, N., ... & Zani, B. (2017). Apathy or alienation? Political passivity among youths across eight European Union countries.European Journal of Developmental Psychology, 1-18, https://doi.org/10.1080/17405629.2017.1404985.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 2 Questionnaires
<p>Abstract</p> <p>WP7 was designed to address questions concerning young Europeans’ EU citizenship, in particular to shed light on factors relevant in processes of construction of citizenship and their joint workings by way of the quantitative analysis of questionnaire data provided by adolescents and young adults in various situations of life across eight EU countries representing variations in, e.g., their economic situation/crisis, political conditions, and their history as EU member state.</p> <p>This work package (WP7) sets out to address open questions concerning factors associated with youth’s active EU citizenship. In particular, assumed (directions of) influences of relevant factors and their joint workings will be examined among adolescents and young adults in various situations of life, across different EU countries representing variations in, e.g., economic situation/crisis, political conditions, and history as an EU member state. This dataset presents the second wave of data collection. WP7 wave 2 data collections have been completed at all eight participating sites in winter 2017/18. The national data sets have been checked and processed including analyses of measurement quality. Finally, a longitudinal cross-national data set has been established which is ready for analyses and available to all Consortium members.</p> <p>The dataset contains more than 7400 questionnaires submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>The data can be reused by researchers who want to compare our data with similar data collected at other points in historical time as well as in other countries than the eight European ones included. Likewise, stakeholders may be interested in reanalyzing our data particularly with a focus on descriptive survey-type information of European citizenship among youth across Europe. Earlier research on these issues is far more restricted in terms of the substantive scope, namely concerning insights into identity and behavioral aspects of participation on the European level and relevant processes, as well as in terms of the cross-national dimension including a larger number of countries from different European regions.</p> <p>File specifics</p> <p>This final working dataset is a cleaned version of raw data. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30 in both waves, or indicated age under 14 or over 30 in one wave and had a missing value on age in the other.</p> <p>Two filter variables based on participants’ age are present in the dataset:</p> <ul> <li>B_Filter_Age1 – selects participants who indicated age between 14-30 or did not indicate age at wave 2</li> <li>B_Filter_Age2 – selects participants who indicated age between 14-30 at wave 2</li> </ul> <p>Other technical variables refer to participants’ unique identification code (ID), country of data collection (Country), and cohort (Cohort; younger versus older). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Longitudinal (Wave 1 and 2) Questionnaires
<p>Abstract</p> <p>WP7 was designed to address questions concerning young Europeans’ EU citizenship, in particular to shed light on factors relevant in processes of construction of citizenship and their joint workings by way of the quantitative analysis of questionnaire data provided by adolescents and young adults in various situations of life across eight EU countries representing variations in, e.g., their economic situation/crisis, political conditions, and their history as EU member state.</p> <p>In the overall design of CATCH-EyoU, WP7 figures as a major link building on the initial theoretical work of WP2 by providing information that speaks to the assumptions based on the literature reviewed and the conceptual ideas developed at that point. Moreover, WP7 draws on results yielded by other work packages as far as available at a given stage of WP7 progress feeding into the construction of questionnaires, hypotheses to be tested, and interpretation of findings, respectively. The goal is to, in turn, contribute to an evidence-based finalization of WP2 conceptual efforts meant to integrate the empirical contributions of the different strands of CATCH-EyoU.</p> <p>At the core of WP7 is a longitudinal assessment using a two wave questionnaire data including a large sample of young people in all countries of the Consortium. After piloting instruments, the first wave of data collection took place in winter 2016/17. Questionnaires, details of the procedure, sample characteristics, and results of preliminary cross-sectional analyses were documented in D7.2 (Findings of wave1: A cross-national report. Noack, Macek 2017). A first set of substantive WP7 wave 1 findings have been presented in a special issue of the European Journal of Developmental Psychology (<a href="https://www.tandfonline.com/toc/pedp20/15/3">Volume 15, 2018 - Issue 3</a>; <a href="https://www.tandfonline.com/doi/full/10.1080/17405629.2017.1391087">Being both – A European and a national citizen? Comparing young people’s identification with Europe and their home country across eight European countrie</a>s. Landberg et.al 2017) as well as in various presentations addressing scientific and broader public audiences. The major reason for establishing a longitudinal data set is the study of processes of the development of major aspects of EU citizenship among youth which entails analyzing the influence of factors such as individual characteristics or conditions met in the family and in school on, e.g., young people’s identification with Europe and political participation. This implies to run analyses that capture the direction of effects as postulated on theoretical grounds which is not possible with a cross-sectional data set but requires sophisticated analyses of longitudinal data.</p> <p>The dataset contains more than 12600 questionnaires submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>The data can be reused by researchers who want to compare our data with similar data focusing on other aspects of attitudinal and identity development in youths. Likewise, stakeholders may be interested in reanalyzing our data particularly with a focus on descriptive survey-type information on the development of European citizenship in adolescence and early adulthood.</p> <p>File specifics</p> <p>This final working dataset is a cleaned version of raw data. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30 in both waves, or indicated age under 14 or over 30 in one wave and had a missing value on age in the other.</p> <p>Three general filter variables based on participants’ age are present in the dataset:</p> <ul> <li>Fltr_age1 – selects participants who indicated age between 14-30 at least in one wave (i.e. filters out all participants with unknown age)</li> <li>Fltr_age2 – selects participants who never indicated age beyond 14-30 (the age might be unknown)</li> <li>Fltr_age3 – selects participants who indicated age between 14-30 at least in one wave and never indicated age beyond 14-30</li> </ul> <p>The last filter variable is the strictest one (it combines the previous two) and it is recommended for use if it is essential that only people from the given age group are included in the analyses.</p> <p>Two additional filter variables based on participants’ age are provided for each wave (A_Filter_Age1, A_Filter_Age2, B_Filter_Age1, B_Filter_Age2). These filters can be used if data from wave 1 or wave 2 are analyzed separately.</p> <p>There are four general variables in the dataset: participants’ unique identification code (ID), country of data collection (Country), participant’s attendance in the study (Attn; indicating his or her participation in wave 1 only, wave 2 only, or both waves), and cohort (Cohort; younger versus older). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Names of variables from wave 1 start with letter “A_”, while names of variables from wave 2 start with letter “B_”.</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 1 Questionnaires: Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia
<p>Abstract</p> <p>This work package (WP7) sets out to address open questions concerning factors associated with youth’s active EU citizenship. In particular, assumed (directions of) influences of relevant factors and their joint workings will be examined among adolescents and young adults in various situations of life, across different EU countries representing variations in, e.g., economic situation/crisis, political conditions, and history as an EU member state. This dataset presents the first wave of data collection. All teams collected data from a quite diverse sample of young people from their respective country.</p> <p>We achieved the targeted sample sizes due to our improved recruitment strategies (based on our experiences from the pilot assessment). More precisely, we could attract more than 10,400 young people to participate in our study (concrete numbers depend on sample selection). Since we initially set out to reach at least 6,400 young people, we were quite successful in our recruitment. Paper-and pencil as well as online modes of assessment proved to be equally effective.</p> <p>The data set integrates data resulting from the first submission wave (wave 1) of Catch-EyoU Questionnaire. The questionnaire was submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>Catch-EyoU Questionnaire was focused on attitudinal, identity, and behavioral aspects of European citizenship, on youth engagement and participation on the regional and national levels as well as on fostering and limiting conditions affecting these. Earlier research is far more restricted in terms of insights into identity and behavioral aspects of participation on the European level and relevant processes, as well as in terms of cross-national comparisons based on a substantial number of countries from different European regions.</p> <p>The data can be reused by researchers who want to compare our data with similar data collected at other points in historical time as well as in other countries than the eight European ones included. Likewise, stakeholders may be interested in reanalyzing our data particularly with a focus on descriptive survey-type information of European citizenship among youth across Europe.</p> <p>File specifics</p> <p>This final working dataset is a cleaned version of raw data. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30 in both waves, or indicated age under 14 or over 30 in one wave and had a missing value on age in the other.</p> <p>Two filter variables based on participants’ age are present in the dataset:</p> <ul> <li>A_Filter_Age1 – selects participants who indicated age between 14-30 or did not indicate age at wave 1</li> <li>A_Filter_Age2 – selects participants who indicated age between 14-30 at wave 1</li> </ul> <p>Other technical variables refer to participants’ unique identification code (ID), country of data collection (Country), and cohort (Cohort; younger versus older). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Pilot Questionnaires
<p>Abstract</p> <p>This final working dataset is a cleaned version of raw data from our pilot study. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30.</p> <p>The data set integrates the data resulting from 1390 Catch-EyoU Pilot Questionnaires submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>The purpose of creating this cross-national data set is to test and compare the quality of items and scales across countries. Given the nature of items and scales a comparable data base does not exist elsewhere.</p> <p>The data can be reused by researchers who want to run more in-depth analyses of the items and scales as well as to a limited extent for substantive analyses.</p> <p>File specifics</p> <p>Technical variables refer to participants’ unique identification code (ID), country of data collection (Country), cohort (Cohort; younger versus older), age group computed based on participants’ age (Agegroup_computed), type of assessment (Assess; paper versus online), and questionnaire version (Version; items were ordered differently between versions A and B). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
voiceHome corpus: A corpus dedicated to distant-microphone speech processing in domestic environments
<p><strong>Purpose: </strong></p> <p>This corpus includes reverberated, noisy speech signals spoken by native French talkers in a lounge and recorded by an 8-microphone device at various angles and distances and in various noise conditions.</p> <p>Room impulse responses and noise-only signals recorded in various real rooms and homes and baseline speaker localization and enhancement software are also provided.</p> <p>This corpus stands apart from other corpora in the field by the number of rooms and homes considered and by the fact that it is publicly available at no cost.</p> <p> </p> <p><strong>Other materials:</strong></p> <ul> <li>Article: N. Bertin, E. Camberlein, E. Vincent, R. Lebarbenchon, S. Peillon, E. Lamandé, S. Sivasankaran, F. Bimbot, I. Illina, A. Tom, S. Fleury and E. Jamet: <a href="https://hal.inria.fr/hal-01343060"><strong>A French corpus for distant-microphone speech processing in real homes</strong></a>, Interspeech2016, Sep 2016, San Francisco, United States, 2016.</li> <li>Related software to reproduce article's results: <ul> <li><a href="https://gitlab.inria.fr/bass-db/mbss_locate">Multi-Channel BSS Locate (v1.3)</a></li> <li><a href="https://gitlab.inria.fr/bass-db/fasst">FASST (v2.2.1)</a></li> </ul> </li> </ul> <p><strong>Documentation (in french):</strong></p> <p>The corpus documentation is both available into the archive and hereafter by clicking on voiceHome_corpus_french_documentation_v1.2.pdf .</p> <p><strong>Terms of use</strong></p> <p>You may exploit the corpus for a non-commercial scientific purpose provided you mention it in any written work or software you derive from its use. Within a published article, paper or report, the corpus must appear in the bibliographical references.</p> <p><strong>Speaker records diffusion consent</strong></p> <p>All participants have given an informed and signed consent about public diffusion of recorded sentences.</p> <p><strong>New corpus version available : voiceHome-2 corpus</strong></p> <p>A new version of the corpus is available : <a href="https://doi.org/10.5281/zenodo.1252143"><strong>voiceHome-2 corpus web page</strong></a></p> <p> </p>
Process plant data integration and querying
<p>This data set includes:</p> <ul> <li>Process plant data derived from heterogeneous sources</li> <li>An OWL ontology of the entities in the data sources</li> <li>Mapping for data in the sources to the ontology (<em>done using Cellfi</em>e,a <em>Protégé plugin. Also save as .json to use</em>)</li> <li>Data integrated from all the sources</li> <li>Sample SPARQL queries (<em>Q5 and Q6</em>)</li> </ul> <p> </p>
Scalasca analysis report of the ASCI Sweep3D benchmark on 65,536 processes in virtual-node mode on IBM Blue Gene/P
<p>A Cube3 performance analysis report written by the Scalasca Trace Analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 65,536 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH.</p>
Scalasca analysis report of the ASCI Sweep3D benchmark on 294,912 processes in virtual-node mode on IBM Blue Gene/P
<p>A Cube3 performance analysis report written by the Scalasca parallel analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 294,912 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH.</p>
Dataset — Revealing the hierarchy of processes and time scales that control the tropic response of shoots to gravi-stimulations
<p>Dataset and python notebook used to produce the figures in the article "Revealing the hierarchy of processes and time scales that control the tropic response of shoots to gravi-stimulations" submited to JexpBot.</p> <p>You can interact directly with the data and python notebook using Binder!</p> <p><a href="https://mybinder.org/v2/git/https%3A%2F%2Fframagit.org%2Fhchauvet%2FDataset_Chauvet-et-Al19/62e33bc0963005fb1483788aa525e92563a6fd16?filepath=Description.ipynb"> Interact with data in Binder</a></p> <p> </p> <p> </p> <p><strong>Data are also in a git repository:</strong></p> <p><a href="https://framagit.org/hchauvet/Dataset_Chauvet-et-Al19">https://framagit.org/hchauvet/Dataset_Chauvet-et-Al19</a></p>
AA5086 tensile tests with Portevin-Le Chatelier (PLC) effect, complete raw dataset: DIC images, raw output and elements of post-processing
<p><em>This dataset is associated with a paper published in Materials Science and Engineering: A.<br> DOI: </em><a href="https://doi.org/10.1016/j.msea.2019.01.009">10.1016/j.msea.2019.01.009</a></p> <p>Hardening and 2D digital image correlation data obtained on AA5086 sheets with Portevin-Le Chatelier (PLC) effect and Piobert-Lüder (PL) bands.</p> <ul> <li>rolled sheets</li> <li>28 tensile tests in rolling direction, room temperature</li> <li>constant imposed velocity with nominal strain rate from 1E-4 to 1E-1 Hz</li> <li>2 geometries: <ul> <li>(mac) large, Lw=60mm</li> <li>(mic or mes) small, Lw=4.2mm</li> </ul> </li> </ul> <p><strong>File contents</strong></p> <ul> <li>summary of experiments <ul> <li>libreoffice database format: experiments-summary.ods</li> <li>specifications, details, measurements and post treated quantities of each test</li> </ul> </li> <li>specimens blueprints (.pdf)</li> <li>video record of a test (.mp4)</li> <li>raw DIC images archives <ul> <li>format: <nominalStrainRate><geometry><index>.zip</li> <li>from 300 to 2300 images per test</li> </ul> </li> <li>time series <ul> <li>archived in timeSeries.zip</li> <li>format: <nominalStrainRate><geometry><index>_timeSeries.dat</li> <li>tabulation separated values, one line per picture (previous item)</li> <li>index, force, cross-head displacement, Stress, axial strain</li> </ul> </li> <li>bands kinetics post-treated quantities <ul> <li>archived in bandKinetics.zip, 2 TSV data files (one per geometry)</li> <li>stress at nucleation, strain magnitude jump and instantaneous working area relative elongation rate.</li> </ul> </li> </ul>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
Dataset from Program Comprehension Study comparing Smalltalk process descriptions to English process descriptions
<p>This dataset includes the materials used in the experiment (questionnaires and documents) as well as raw data and pre-processed data.</p>
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>
Some atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain 2-D fields of atmospheric T and P maps, as climatologies averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, and from the CESM Large Ensemble control simulation (LENS), the basis for two of the plots in our manuscript that is, as of January 2019, in review at JAMES. All of these data files are binary, written sequentially, 288 x 192. Also included are some PowerPoint-generated pdf images of the two fields.</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.