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10,119 results for “IT Support”
Competences and Needs of Students and Support Received / Competencias y Necesidades de los Alumnos y Apoyos que Reciben // STUDENT-INFORM_Data_CC-CS_T2-Pre
<p>Information provided by the teacher on the competencies, needs and individual supports of the students in his/her classroom. The teacher estimates the competence in 5 areas (Social, Mathematics, Spanish Language, Valencian Language and Physical Education), the presence of educational needs (motor, intellectual, hearing, vision, hearing and language, learning, behavioral, developmental, and high abilities), and the type of support received and with what periodicity (therapeutic pedagogy, compensatory education, hearing and language and educational reinforcement). The information was collected through interviews with tutor teachers from public schools in Castellón and Seville (Spain) in the fall of the school year 2010-11 and compiled in this Descriptive Data, Competencies and Educational Needs of the Student Report (STUDENT-INFORM) (García Bacete et al., 2014). The dataset also includes information on the worries that some students generate in the teacher in different areas (relational, behavioral, and academic) and the specific reasons why these students worry the teacher, compiled in the Class Group and Teacher Report (CLASSROOM-INFORM) (García Bacete et al., 2014). To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a></p> <p>Información proporcionada por el profesor sobre las competencias, necesidades y apoyos individuales de los alumnos de su aula. El profesor estima la competencia en 5 áreas (Social, Matemáticas, Lengua Española, Lengua Valenciana y Educación Física), la presencia de necesidades educativas (motrices, intelectuales, auditivas, visuales, de lenguaje, de aprendizaje, de conducta, de desarrollo y de altas capacidades), y el tipo de apoyo recibido y con qué periodicidad (pedagogía terapéutica, educación compensatoria, audición y lenguaje y refuerzo educativo). La información se recogió a través de entrevistas a profesores tutores de colegios públicos de Castellón y Sevilla (España) en el otoño del curso escolar 2010-11 y se recopiló en este Informe de Datos Descriptivos, Competencias y Necesidades Educativas del Alumno (STUDENT-INFORM) (García Bacete et al., 2014). El fichero de datos también incluye información sobre las preocupaciones que algunos alumnos generan en el profesor en diferentes ámbitos (relacional, conductual y académico) y los motivos concretos por los que estos alumnos preocupan al profesor, recopilados en el Informe del grupo de clase y del profesor (CLASSROOM-INFORM) (García Bacete et al., 2014). Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Supporting data for Herviou and Bonnet (2023)
<p>Supporting data for <strong>Herviou C.</strong>, Bonnet G. (<em>2023</em>). Paleocene-Eocene high-pressure carbonation of Western Alps serpentinites: positive feedback between deformation and CO<sub>2</sub>-CH<sub>4</sub> fluid ingression responsible for slab slicing? Geochemistry, Geophysics, Geosystems.</p>
Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.
<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>
Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis
<p>The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in<br> simulations of electrocatalysis".</p> <p>Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.</p>
Supporting Files: Simultaneous creation of a large vapor plume and pumice raft by the 2021 Fukutoku-Oka-no-Ba shallow submarine eruption
<p>Supporting code and data for 2023 EPSL publication, "Simultaneous creation of a large vapor plume and pumice raft by the 2021 Fukutoku-Oka-no-Ba shallow submarine eruption." The data file FOB_Compiled_Plume_Height_Lighting_Data.zip is redundant with data included in the supplement of the EPSL publication.</p>
Dataset and code supporting Cornelis et al. 2023. Stuck in the weeds: Invasive grasses reduce tiger snake movement
<p>Data and code used in the publication:</p> <p>Cornelis, J., Cooper, C. E., Lettoof, D. C., Mayer, M., Marshall, B. M. 2023 Stuck in the weeds: Invasive grasses reduce tiger snake movement. bioRxiv 2023.03.06.531246; doi: https://doi.org/10.1101/2023.03.06.531246</p> <p>Includes telemetry data, aKDE, dBBMM, and Bayesian model specification and results, code to reproduce analysis and generate figures </p> <p> </p> <div> </div>
Fig. 2. A in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 2. A haplotype network based on cytochrome oxidase I (CO1) fragment using 112 previously published GenBank sequences and representing 20 genetic clades numbered as in Palandačić et al. (2017, 2020), of which 12 are considered valid species including P. chrysoprasius (Pallas, 1814) and Kuban' Phoxinus (an available species name not known). The lines carry the number of mutational steps (shown in red).
Fig. 1 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 1. Map of the localities of Phoxinus Agassiz, 1835 in river drainages of the northern and north-eastern coasts of the Black Sea and the Caspian Sea based on numerous published sources (references available in Supp. file 1) and public museum collections (Museum of Zoology, National Museum of Natural History, Kyiv, Ukraine; Natural History Museum, Vienna, Austria; Zoological Research Museum Alexander Koenig, Bonn, Germany). Coloured circles and numbers correspond to genetically examined individuals of clades specified in Palandačić et al. (2017, 2020): yellow circle = P. marsilii, Clade 9; black circles = 'Baltic Phoxinus', Clade 17; grey circles = P. colchicus, Clade 18; purple circle = 'Kuban Phoxinus', Clade 19; pink circle = 'Crimean Phoxinus', Clade 20.
Fig. 4 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 4. DFA based on 11 counts, two coded characters, and 55 relative measurements, for males and females separately (A) and for females only (B). Numbers of samples or clades as in Fig. 3. Abbreviations: f = females; m = males. DFA statistics values: A. Wilks' Lambda 0.00000, approx. F (156.189) = 11.390, p <0.0000. B. Wilks' Lambda 0.00002, approx. F (48.48) = 36.279, p <0.0000 (perfect discrimination).
Fig. 3 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 3. DFA based on 11 counts and 2 coded characters as in Supp. file 4. Clade 5 = 5a, Danubian tributaries in Bulgaria (Nishava, Beli Vit, and Palakaria samples and P. csikii Hankó, 1922 from type locality). Clade 14 = non-Danubian rivers of the Black Sea coast in Bulgaria (14a, Izvorska, Veleka, Karaagach and Kamchiya) and Turkey (14b, Gönen and Sapanca). Clade 9 = P. marsilii Heckel, 1836. Clade 20 = Crimea, Salhir. DFA statistics values: Wilks' Lambda 0.16694, approx. F (45.1345) = 14.709, p <0.0000 (perfect discrimination).
Fig. 5. A in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 5. A. Neotype of Phoxinus chrysoprasius (Pallas, 1814), ♂ (ZFMK 93640-59). B. ♀ (ZFMK 93640- 59), 87.2 mm SL, same locality and date as the neotype.
Datasets and codes for De Lorm et al. 2023: Optimising the automated recognition of individual animals to support population monitoring
<p>Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, non-invasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogues is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages.</p> <p> </p> <p>In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I<sup>3</sup>S-Pattern, and WildID. As a case study, we consider the African wild dog <em>Lycaon pictus</em>, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intra-specific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat colouration patterns. </p> <p> </p> <p>The process of selecting suitable images was automated using Convolutional Neural Nets that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). </p> <p> </p> <p>Our automated image pre-processing has immediate application for expanding monitoring based on image-matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts. </p>
Supporting data for 'Chemically Reduced Graphene Oxide based Aerogels (rGOAs) - insight on the surface and textural functionalities dependent on handling the synthesis factors'
<p>Experimental data for the 'Chemically Reduced Graphene Oxide based Aerogels (rGOAs) - insight on the surface and textural functionalities dependent on handling the synthesis factors' manuscript/publication.</p> <p>Package contains following data:<br> 1. File with description of the experimental conditions for rGOAs synthesis, format: .pdf, number of files: 1 <br> 2. Data of Boehm titration for graphene oxide used for synthesis of rGOAs, format: .txt, number of files: 12<br> 3. Fourier-transform infrared spectra of rGOA samples and GO used for synthesis, format: .csv, number of files: 16<br> 4. Raw chromatograms of test probes for rGOA samples, format .txt, number of files: 15 folder with 11 files in each</p> <p> </p>
Urban cloud patterns and environmental factors dataset supporting for paper: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023)
<p> </p> <p>The dataset supporting for the publication: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023), PNAS. </p> <ul> <li>The data format is in tabular form (.csv, comma delimited). Each row is the <strong>monthly </strong>aggregated <span class="math-tex">\(\Delta_{CloudCover}\)</span> for <strong>each </strong>city for daytime and nighttime, associated with the environmental factors used to study the relationship in the research. More specifically, the description of each factor associated with its unit formatted in the table are listed as follows: <ul> <li>the spatial differences in cloud coverage (in percent) between urban and adjacent background domain (<span class="math-tex">\(\Delta_{CloudCover}\)</span>): <strong>cldmeand </strong>(in percent) </li> <li>the city size (<span class="math-tex">\(log_{10}A\)</span>, A in km<sup>2</sup>) as an indicator of surface roughness and anthropogenic emission: <strong>CitySize </strong>(in <span class="math-tex">\(log_{10}(km^{2})\)</span>)</li> <li>the differential urban-background surface heating measured by land surface temperature (<span class="math-tex">\(\Delta LST\)</span>): <strong>Tempdiff </strong>(in K)</li> <li>the moisture availability measured by annual precipitation (P): <strong>precipannualbackground </strong>(in mm)</li> <li>the energy availability measured by mean annual temperature (<span class="math-tex">\(\overline{T}\)</span>): <strong>tempannualbackground </strong>(in <span class="math-tex">\(^{o}C\)</span>)</li> <li>the longitudes and latitudes for the defined city: <strong>xcoords </strong>(longitudes, in meters) and <strong>ycoords </strong>(latitudes, in meters)</li> </ul> </li> <li>Other columns: <ul> <li><strong>NAME10</strong>: the name of urban domain or city defined in the study (CENSUS 2010)</li> <li><strong>time</strong>: daytime (around 13 LT) or nighttime (around 1 LT). </li> <li><strong>monthtext</strong>: specifies the month (January to December)</li> <li><strong>urban_type</strong>: the geographical location of the defined urban area (either inland, coastal or mountainous city) </li> </ul> </li> </ul>
Supporting data for the manuscript "Protein quality assessment with graph convolution guided by a loss function designed for high quality decoys"
<p>This dataset provides the predictions of the protein quality<br> assessment method Q_epsilon with respect to CASP13 and CASP14, as well<br> as a snapshot of the github repository providing the code for the<br> method.<br> The format of the file is as follows: The first column represents the<br> target and decoy names in the format <target name>_<decoy name>. The<br> second column is the true GDTTS, and the third column represents the<br> predicted GDTTS by Q_epsilon.</p>
Research data supporting: "Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"
<p>This repository contains the set of data shown in the paper <strong>"Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"</strong>, published on The Journal of Chemical Physics (DOI:10.1063/5.0139010)</p>
Supporting information for a multifidelity neural network formulation for molecular potential energy surfaces
<p>This is a supplementary information for our paper titled "<em>Multifidelity neural network formulations for prediction of quantum chemistry potential energy surfaces</em>"</p> <p>Supplemental information includes two data files corresponding to the complete sets of low and high fidelity training data used in numerical experiments. Format is JavaScript Object Notation (JSON).</p> <p>1. low_fidelity_training_data.json contains 74000 records</p> <p>2. high_fidelity_training_data.json contains 36988 records</p> <p>Each record consists of a numerical id ("id"), (x,y,z) position tuples ("geometry") for C5H5 ordered as 5 carbon atoms followed by 5 hydrogen atoms, and corresponding potential energy ("energy").</p> <p>Source: normal mode sampling around 2 wells, 1 transition state, and a set of IRCs as depicted in Figure 2.</p> <p>Usage: subsets of this data were used as needed to define different data amounts and different subset randomizations in Figures 5 through 8.</p>
Kinetic in support of "Pre-Steady-State Kinetic Characterization of an Antibiotic-Resistant Mutant of Staphylococcus aureus DNA Polymerase PolC"
<p>These are KinTek Explorer mechanism files containing the data and analysis described in the manuscript "Pre-Steady-State Kinetic Characterization of an Antibiotic-Resistant Mutant of Staphylococcus aureus DNA Polymerase PolC" (bioRxiv 2022.10.04.510889; doi: https://doi.org/10.1101/2022.10.04.510889). </p>
Dataset in support of "Laboratory wave and stress measurements quantify the aerodynamic sheltering in extreme winds" by Tan et al. (2023, JGR: Oceans)
<p><strong>Data introduction:</strong></p> <p> There are three datasets used in this research: dataset 1 from Wind-Only (WO) experiment, dataset 2 from JONSWAP experiment with 10-cm significant wave height (J10), and dataset 3 from monochromatic wave experiment with 7.5-cm amplitude (M7.5).</p> <p> Each dataset contains quality-controlled data of the respective experiment mentioned above. The data files are in the mat (MATLAB) format. There are 9 mat files in each dataset, and each file represents data collected under a specific wind forcing condition, with the fan frequency in the 10-50 Hz range with 5 Hz interval.</p> <p> Each file contains four variables: <em>seg</em> (water elevation time series collected by the wave-wire with the units of <em>m</em>, demeaned and detrended), <em>U</em> (along-tank, downwind component of wind sampled by the IRGASON anemometer with the units of <em>m/s</em>), <em>V</em> (cross-tank component of wind sampled by the IRGASON anemometer with the units of <em>m/s</em>), and <em>W</em> (vertical component of wind collected by the IRGASON anemometer with the units of m/s). All four variables were collected at a sampling frequency of 20 Hz.</p>
Recalibration of the lunar chronology due to spatial cratering-rate variability - Supporting Information
<p>CR_moon.csv: Relative cratering rate shown in Fig.3 and 5.a. The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</p> <p>SI_convert_age.m: Matlab code computing model ages of Plutarch and Kirkwood craters using the chronology function presented in this study.</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.