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15,733 results for “article”
FTIR dataset from the article "Resistance to Degradation of Silk Fibroin Hydrogels Exposed to Neuroinflammatory Environments"
<p>The attached archive encounters the FTIR data used for the calculation of the β-sheet content of the silk fibroin hydrogels in the in-vitro experiments. Such data is used in main Figures 2<strong>c</strong> and 2<strong>d</strong>, 4<strong>a </strong>and 7<strong>a</strong> as well as Supplementary Figure 2<strong>a</strong> of the associated article.</p> <p> </p> <p>The acquisition of the files has been performed as indicated below:</p> <ul> <li><strong>Equipment:</strong> Thermo Scientific™ Nicolet™ iS™ 5 Spectrometer (Thermofisher, United States)</li> <li><strong>Acquisition Software:</strong> OMNIC 9.2.86 (Thermofisher, United States)</li> <li><strong>Number of Scans:</strong> 64</li> <li><strong>Data Spacing: </strong>0.482 cm<sup> -1</sup></li> </ul> <p> </p> <p>The files have been reported as raw complete spectrum including the absorbances in the wavenumbers from 600 to 1500 cm<sup> -1</sup> in the format of “.SPA”. In the following lines, we provide examples of software to process and analyze the spectra files included in the database.</p> <ul> <li>Python version 3.8 to 3.10 upon the availability of the “SpectroChemPy” (Travert and Christian, 2023) function.</li> <li>Matlab R2016a and newer upon the availability of the “LoadSpectra” (Oldenburg, 2023) function.</li> <li>OMNIC 9 (Thermofisher, United States).</li> <li>Essential FTIR (Operant LLC, United States).</li> </ul> <p> </p> <p>Provision of the data in other formats is available upon request. Inquiries may be directed to <a href="mailto:daniel.gonzalez@ctb.upm.es?subject=FTIR%20Samples%20Format">Daniel González-Nieto</a> or <a href="mailto:mahdi.yonesi@ctb.upm.es?subject=FTIR%20Samples%20Format">Mahdi Yonesi</a>.</p> <p><strong>References:</strong></p> <p>OLDENBURG, K. 2023. LoadSpectra. MATLAB Central File Exchange.</p> <p>TRAVERT, A. & CHRISTIAN, F. 2023. SpectroChemPy, a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python. Github.</p> <p> </p>
Dataset of the Journal article "Energy duty in direct contact membrane distillation of hypersaline brines operating at the water-energy nexus"
<p>This repository contains the Dataset of the Journal article "Energy duty in direct contact membrane distillation of hypersaline brines<br> operating at the water-energy nexus" published on Journal of Membrane Science. </p>
Supplementary material for article: A database driven excavation of a waterlogged Neolithic settlement. The case of Küssnacht-Immensee Dorfplatz (Switzerland).
<p>These are all necessary data to produce 3D models of the excavated Neolithic waterlogged site of Küsnacht-Immensee Dorfplatz in the Canton Schwyz in Switzerland. Sediment deposits and find amounts are given as tables for GIS use in the txt-files. The codes for the semiquantitative description of the sediment are explained in the codes-pdf. The database model for the data acquisition of the sediment deposits and finds is given as accdb. Piles and horizontal wooden elements are given in the excel-file. </p> <p>Sediment profile photographies and the readymade 3D-model are given separately in 10.5281/zenodo.6536246.</p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p> </p>
Data used in the figures of the article "A cool runaway greenhouse without surface magma ocean"
<p>These data have been generated with the codes Exo k and PCM.</p> <p>Exo k is an open-source software. A complete documentation on how to install and use it can be found at http://perso.astrophy.u-bordeaux.fr/∼jleconte/ exo k-doc/index.html.<br> The generic Global Climate Model PCM (formerly known as LMDZ.generic) used in this work is the version 2528 that can be downloaded with documentation from the SVN repository at https://svn.lmd.jussieu.fr/Planeto/trunk/ LMDZ.GENERIC/. More information and documentation are available at http://www-planets.lmd.jussieu.fr.</p> <p>The archive is organized as follows:<br> ├─ Fig1 (comparison of atmospheric profiles with a same Outgoing Thermal Radiation)<br> │ ├─ OTR274.txt (profiles with a 274 W/m2 bolometric emission)<br> │ ├─ OTR10000txt (profiles with a 1000 W/m2 bolometric emission)<br> │<br> ├─ Fig2 (Outgoing Thermal Radiation as a function of surface temperature)<br> │ ├─ Geoflux=0 (internal heat flux = 0 W/m2)<br> │ │ ├─ T1_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ Proxima_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ M3_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ Sun_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ F1_0_ISR_Tsurf_OTR_ALB.dat<br> │ ├─ Geoflux=0.09Wm-2 (internal heat flux = 0.09 W/m2)<br> │ ├─ T1_0.09_ISR_Tsurf_OTR_ALB.dat<br> │ ├─ Sun_0.09_ISR_Tsurf_OTR_ALB.dat<br> │<br> ├─ Fig3 (Trappist-1b profiles and spectra)<br> │ ├─ Profiles<br> │ │ ├─ Adiabatic<br> │ │ │ ├─ Trappist1b_adiabatic_profile_1EO.txt (1x Earth Ocean)<br> │ │ │ ├─ Trappist1b_adiabatic_profile_3EO.txt (3x Earth Ocean)<br> │ │ │<br> │ │ ├─ Converged<br> │ │ │ ├─ Trappist1b_profile_1EO_geoflux=0W.txt (1x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_profile_1EO_geoflux=5W.txt (1x Earth Ocean, internal heat flux = 5 W/m2)<br> │ │ │ ├─ Trappist1b_profile_3EO_geoflux=0W.txt (3x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_profile_3EO_geoflux=5W.txt (3x Earth Ocean, internal heat flux = 5 W/m2)<br> │ │ │<br> │ ├─ Spectra<br> │ │ ├─ Adiabatic<br> │ │ │ ├─ Trappist1b_adiabatic_spectra_1EO.txt (1x Earth Ocean)<br> │ │ │ ├─ Trappist1b_adiabatic_spectra_3EO.txt (3x Earth Ocean)<br> │ │ │<br> │ │ ├─ Converged<br> │ │ │ ├─ Trappist1b_spectra_1EO_geoflux=0W.txt (1x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_spectra_1EO_geoflux=5W.txt (1x Earth Ocean, internal heat flux = 5 W/m2)<br> │ │ │ ├─ Trappist1b_spectra_3EO_geoflux=0W.txt (3x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_spectra_3EO_geoflux=5W.txt (3x Earth Ocean, internal heat flux = 5 W/m2)<br> │<br> ├─ Fig4_(net stellar and emission fluxes for a converged and adiabatic profiles)<br> │ ├─profiles_Sun_adia_converged_netfluxes.txt<br> │<br> ├─ Fig5 (Profiles for different instellations, for each star)<br> │ ├─ T1_profiles.txt<br> │ ├─ Proxima_profiles.txt<br> │ ├─ M3_profiles.txt<br> │ ├─ K5_profiles.txt<br> │ ├─ Sun_profiles.txt<br> │ ├─ F1_profiles.txt<br> │<br> ├─ Fig6 (Comparison 1D vs 3D)<br> │ ├─ 1D<br> │ │ ├─ exok_1D_PTH_trappist1.txt<br> │ │ ├─ exok_1D_PTH_Proxima.txt<br> │ │ ├─ exok_1D_PTH_M3.txt<br> │ ├─ 3D<br> │ ├─ PT_trappist1_3D.dat<br> │ ├─ PT_Proxima_3D.dat<br> │ ├─ PT_M3_3D.dat<br> │ ├─ HeatingRates_3D_trappist1.dat<br> │ ├─ HeatingRates_3D_trappist1.dat<br> │ ├─ HeatingRates_3D_trappist1.dat<br> │<br> ├─ Fig7 (Profiles for 2 values of instellation and 3 values of cp)<br> │ ├─ profiles_cp_Sun.txt <br> │<br> ├─ Fig8 (Profiles for 3 values of instellation and continuums from 2 version of MT_sCKD)<br> │ ├─ profiles_2continuums_Sun.txt <br> │<br> ├─ Fig9 (Profiles as a function of surface pressure and internal heat fluxes)<br> │ ├─ EarlyVenus<br> │ │ ├─ profiles_fig9_EarlyVenus_0.1bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_1bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_10bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_100bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_270bar.txt<br> │ ├─ Trappist1<br> │ ├─ profiles_fig9_Trappist1_0.1bar.txt<br> │ ├─ profiles_fig9_Trappist1_1bar.txt<br> │ ├─ profiles_fig9_Trappist1_10bar.txt<br> │ ├─ profiles_fig9_Trappist1_100bar.txt<br> │ ├─ profiles_fig9_Trappist1_270bar.txt<br> │<br> ├─ readme.txt</p> <p> </p> <p> </p>
Resources for the article "Investigating the role of educational robotics in formal mathematics education"
<p>This repository contains the material required to reproduce the study looking to investigate the role of educational robotics in formal mathematics education for 15 year old students in the French speaking region of Switzerland. This includes :</p> <ul> <li> <p>Pedagogical content in the form of both teacher and student resources</p> </li> <li> <p>Data collection ressources (surveys and tests)</p> </li> </ul> <p>If you use any of the resources provided in this repository, please cite the following</p> <p>• The Zenodo repository, DOI: 10.5281/zenodo.4649842</p> <p>• The corresponding article : Brender, J., El-Hamamsy, L., Bruno, B., Chessel-Lazzarotto, F., Zufferey, J.D., Mondada, F. (2021). Investigating the Role of Educational Robotics in Formal Mathematics Education: The Case of Geometry for 15-Year-Old Students. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds) Technology-Enhanced Learning for a Free, Safe, and Sustainable World. EC-TEL 2021. Lecture Notes in Computer Science(), vol 12884. Springer, Cham. https://doi.org/10.1007/978-3-030-86436-1_6</p> <p>• Licence : CC-BY</p>
Data set for the journal article: Social life cycle assessment of green methanol and benchmarking against conventional fossil methanol
<p>Single File containing:</p> <ul> <li>Green Methanol Inventories: numerical data as displayed in Figure 4, Main social life cycle inventory data of the green methanol system. </li> <li>Conventional Methanol Inventories: numerical data as displayed in Figure 5, Main social life cycle inventory data of the conventional methanol system. </li> <li>Supplementary information: Diagrams and tables describing teh flowsheet of the simulations used in this work: <ul> <li> <p>Green methanol production process (flowsheet and stream table)</p> </li> <li> <p>Syngas production through Steam Methane Reforming (flowsheet and stream table)</p> </li> <li> <p>Conventional methanol production process (flowsheet and stream table)</p> </li> </ul> </li> </ul>
Selection process of articles for a systematic literature review on citizen science initiatives in school contexts
<p>Selection process of articles for a systematic literature review on citizen science initiatives in school contexts. Initial records are extracted from Web of Science database from 2000 to 2021. We used the keyword "citizen science" in the title mixed with some keywords in the topic of the search: “classroom” or “school” or “student*” or “pupil” or “learning”.</p> <p>The different database sheets inform about the application of the inclusion and exclusion criteria applied.</p>
Dataset of article: Investigating Developers' Perception on Success Factors for Research Software Development
<p>This dataset is an addendum to the article "Investigating Developers' Perception on Success Factors for Research Software Development" to provide information regarding the anonymously collected data.</p> <p> </p> <p> </p> <p> </p>
Data underlying the article: 3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors
<p>This repository contains the datasets and results supporting the conclusions of the manuscript "<strong>3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors</strong>". </p> <p>Publicly available data is not included in this repository. The source code to generate the results gathered here can be found on GitHub (https://github.com/CDDLeiden/3ddpd). </p>
Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay - data from the article published in PlosOne
<p>Data that have been used in the manuscript "Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay" by M. Fontaine et al., published in PlosOne in 2023.</p>
DATASET WITH CLASSIFICATION OF ACCOUNTS THAT DISCLOSE SCIENTIFIC ARTICLES ON TWITTER BETWEEN BOT, HUMAN AND CYBORG THROUGH BOTOMETER
<p>This dataset is the result of research that analyzed accounts that tweeted scientific articles in order to classify them as bots, humans or cyborgs through Botometer. Three articles were selected among those with the highest scores on the Altmetric platform and a sample of 18,316 Twitter user accounts were classified.</p>
Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts
<p>This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review.</p> <p>It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>).</p> <p> </p> <p>Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4):</p> <p><strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N)</p> <p><strong>Attribute description</strong></p> <p>Id - Identifier of polygons</p> <p>gridcode - Code of agricultural archetypes of Levers et al., (2018)</p> <p>M_ZHYD: Unique identifier of corresponding FEC</p> <p>mars_bt12: River types</p> <p>eco_stat_2: Ecological status</p> <p>Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean</p> <p>Cum_pressu: Agricultural pressure index</p> <p>Nitrogen: Agricultural nitrogen pressure</p> <p>Pesticides: Agricultural pesticide pressure</p> <p>Hydromorph: Agricultural hydromorphological pressure</p> <p>Water_abst: Agricultural water abstraction</p>
Open Data of the article "Literal vs. default translation. Challenging the constructs with Middle Egyptian translation as an extreme case in point"
<p>The file contains the datasets and the code generated for the analyses presented in the article "Literal vs. default translation. Challenging the constructs with Middle Egyptian translation as an extreme case in point", to be published in <em>Sendebar</em> in 2023.</p> <p> </p> <p>Abstract of the article:</p> <p>This paper presents the results of a study that compares the constructs of literal translation (Schaeffer & Carl, 2014) and default translation (Halverson, 2019) by means of an observational, exploratory study with Middle Egyptian translation as an extreme case in point. Two graduating students of the MA in Egyptology at Universitat Autònoma de Barcelona and three recent graduates of the same MA took part in the study. They translated two excerpts from two Middle Egyptian literary texts into Spanish. InputLog was used to collect translation-process data and derive word-level indicators of cognitive effort (typos per word, word typing speed, and within-word pause) from them. Results showed a clear link between default translations and cognitive effort (low number of typos, low number of respites, and fast writing speed). However, the assumption that deviations from literality cause greater cognitive effort was not observed. Hence, default translation may serve as a more adequate construct to describe the regular way translators perform.</p>
Data set for the article "Synchronous replication initiation of multiple origins"
<p>This data set contains the data of the submitted article "Synchronous replication initiation of multiple origins". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli initiates DNA replication at multiple origins synchronously in fast growth conditions. We study by mathematical modelling under what conditions replication is initiated synchronously.</p>
Published articles on gender in cultural and creative sectors
<p>Results from the analysis of 6.631 scientific articles on Gender in Cultural and Creative Sectors (CCS) retrieved from Web of Science and dating between 1958 and 2022. The largest number of publications have their focus on the audio-visual and multimedia industry (37,08%), followed by performing arts (20,48%), book and press (17,80%) and visual arts (9,35%). In other CCS, the share of publications ranges between 5,35% (advertising) and 0,41% (arts and crafts).</p> <p>Therefore, there is an evident need to foster research on the women's role in particular domains of the CCS, such as heritage, library, archives, architecture and, especially, arts and crafts. Additionally, the role of women is less emphasized in the preservation and conservation stage of the CCS value chain (for almost all the domains), in the management and regulation stage (for visual and performing arts) and in the creation stage (in the audio-visual and multimedia sector).</p> <p>A guide on Gender integration Cultural statistics can be retrieved at this URL: https://www.mesoc-project.eu/resources/gender-and-cultural-and-creative-sector-guide-gender-integration-cultural-statistics</p> <p> </p>
Data, scripts, and figures of the article: Evaluation of oregano essential oil in broilers challenged with a mixed Eimeria spp. and high dietary protein model of subclinical coccidiosis
<p>Data, scripts, and figures of the article "Evaluation of oregano essential oil in broilers challenged with a mixed Eimeria spp. and high dietary protein model of subclinical coccidiosis" to be published in the journal Animal - Open Space. </p>
Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"
<p>Article:<em> </em><a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a></p> <p>The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
Dataset to the article "Omnidirectional endpoint force control through Functional Electrical Stimulation" by Marek Sierotowicz and Claudio Castellini
<p>The present .zip files contains the anonimized datasets of a static force output control test facilitated through FES.</p> <p>In addition, the DataExtraction MATLAB code is meant to provide the synchronisation offsets for the logs.</p> <p>The data is sampled from 3 anonymized participants, who all signed informed consent forms.</p> <p>The experiment was carried out in compliance with the standards of the Declaration of Helsinki, and was approved by the ethical committee of the Friedrich-Alexander Universität Erlangen-Nürnberg (approval ID 22-304-S).</p> <p> </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.