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7,195 results for “SET”
CRDM@NCRC Test Data Set Wizard and Witch vaccination project against dragon pox
<p>This is a dummy data set to test restricted access. We created test data about vaccination of wizards and witches against dragon pox. The data set contains over 60 entries of the vaccination.</p>
Data Set on Local Government Indicators in Chile
<p><strong>Data Set on Local Government Indicators in Chile</strong></p> <p>This repository contains a dataset in progress on local government indicators. The open-access version is available at <a href="https://doi.org/10.5281/zenodo.7568387">https://doi.org/10.5281/zenodo.7568387</a></p> <p><strong>GitHub repository:</strong> <a href="https://github.com/bgonzalezbustamante/local-gov-indicators">https://github.com/bgonzalezbustamante/local-gov-indicators</a></p>
Data set for thermal transport study in single-band Hubbard Model using DQMC: Lorenz number and Wiedemann-Franz law
<p>Data set for thermal transport study in single-band Hubbard Model using DQMC: Lorenz number and Wiedemann-Franz law</p> <p>Labeling in plots are slightly different with the preprint paper.</p>
SMARTFARM Eddy Covariance Demonstration Data Set of Five Agro-ecosystem Sites
<p>Demonstration data set from five eddy covariance towers at agro-ecosystem sites that are funded under the DOE ARPA-E SMARTFARM program in Phase I. Data set is provided to restricted users for the purpose of operational and performance qualification only. Data sets include 13-26 weeks of all 30 minute interval data for all sensor modalities per the sensor workbook and FOA. Full data sets will be published through Ameriflux at https://ameriflux.lbl.gov/ starting August 12, 2022, with annual updates thereafter. Some data, such as methane and nitrous oxide are embargoed.</p> <p>This work was funded through the U.S. Department of Energy, ARPA-E, under Cooperative Agreement DE-AR0001228 led by ARVA Intelligence Corp. (https://www.arvaintelligence.com) in collaboration with Lawrence Berkeley National Laboratory (LBNL).</p>
Data set of Sepsis awareness at the university hospital level: a survey-based cross-sectional study
<p><strong>This is the dataset for the SAfE survey</strong></p> <p>We conducted a survey on nurses and physicians distributed over all adult departments of the Lausanne University Hospital (LUH) and local paramedics. The survey aimed to assess professionals’ demographics (age, profession, seniority, unit of activity), prior sepsis education, perceptions, knowledge of sepsis epidemiology, definition, recognition and management. Correlation between surveyed personel and sepsis perceptions and knowledge were assessed with univariate logistic regression models.</p> <p><strong>Methods:</strong></p> <p>The research team designed a survey inspired from previously published surveys assessing knowledge and awareness of sepsis.</p> <p>The questions were tailored to the profession (clinical scenario adapted activity sector - medicine, surgery, emergency department or gynecology). The survey was written and completed in French. Each section of the survey (paramedics’, nurses’ and physicians’ section) was submitted to three focus groups consisting of 3 to 6 participants of each profession, commonly involved in care of patients with sepsis. These focus groups assessed the applicability and appropriateness (validity) of the survey. The focus group were constituted of nurses, physicians and paramedics of all seniority levels. Their primary task was in assessing whether formulations and relevance of questions were adequate. The survey was revised using feedback from the groups. Surveys of nursing staff and paramedics were more focused on screening, initial evaluation and early management whereas physicians were also tested on diagnosis and management. Responses options included Likert-type scales, binary (e.g. “yes/no”) or multiple choices. Each question was locked upon answering, which prevented post hoc changes that could be influenced by information provided at a later stage of the survey. The final survey contained questions on participants’ demographic characteristics (5/7/6 questions for nurses/paramedics/physicians), sepsis continuous education (3/3/3 questions), self-evaluation of sepsis knowledge and clinical management (2/2/2 questions), definitions, scores and epidemiology (11/12/14 questions), and sepsis management (4/4/5 questions). The survey was developed in REDCap (Research Electronic Data Capture) software so as to automatically export participants’ responses to a database. Surveys are provided as supplementary material (supp. meth. survey).</p> <p>Participants were recruited between January 20 and October 10, 2020. We aimed for a convenience sample size of 1,000 persons (approx. 20% of the active HCPs) distributed over all departments (Emergency department (ED), intensive care unit (ICU), Medicine, Paramedic, Psychiatry, or Surgery) and professions (paramedics, nurses and physicians) to reach 20% of LUH staff considered HCPs, being as representative as possible. Pediatrics and neonatology staff (not covered by Sepsis-3 consensus definitions) as well as nurses and physicians not in daily contact with patients (i.e., who were working in research team or in administration) were excluded. We favored a supervised approach rather than a dissemination of the survey to all HCPs by email. Participants answered the online survey under trained interviewer supervision so as maximize data quality and to avoid biased responses (internet queries, discussions between colleagues). Furthermore, to avoid multiple answers by a same HCP, surveys were accessed by QR-code only available at screening; timing of survey completion was registered and email addresses were registered.</p> <p>Thus, participants were screened amongst the medical (n=1664) and nursing staff (n=2463) in daily contact with patients of LUH and amongst paramedics of the Canton of Vaud (n=290) during the screening period. Screening by trained interviewers took place during scheduled patient hand-offs, seminars or group meetings, as permitted by heads of units. Participation was voluntary and anonymous. Participants completed the online survey using tablets or smartphones (participants’ or provided by the investigators).</p> <p><strong>Results:</strong></p> <p>Between January and October 2020, 1,116 of 1,216 contacted professionals completed the survey (participation rate 91.8%). These participants represented about 25% of the workforce (n=4417) – i.e., 25.1% of nurses (619/2,463), 20.9 % of physicians (348/1,664),and 51.4% of paramedics (149/290). Only 13% of participants (physicians: 28.4%, nurses: 5.9%, paramedics: 6.8%) correctly identified the Sepsis-3 consensus definition. Similarly, less than 50% of participants (physicians: 48.6%, nurses: 10.0%) selected the SOFA score as a sepsis defining score for infected patients. Furthermore, 24% of participants properly identified the qSOFA score as a predictor of increased mortality; and 6% selected correctly its components. For a suspected sepsis, 96.1%, 91.6% and 75.8% of physicians respectively chose blood cultures, broad-spectrum antibiotics and fluid resuscitation as required interventions; 76.4% and 18.2% of physicians requested initial measures within 1 and 3 hours, respectively. For physicians, recent training correlated with awareness regarding definitions, SOFA and qSOFA score use and components: ORs (95%CI) 2.2 (1.4-3.6), 4.3 (2.7-6.7), 3.4 (2.2-5.2), and 2.6 (1.5-4.6), respectively).</p> <p><strong>Conclusions:</strong></p> <p>We identify a deficit of awareness among physicians, nurses and paramedics at LUH correlating with a lack of sepsis-specific training.</p>
Data set for "Stability limitations of optical frequency transfer in telecommunication DWDM networks"
<p>Here we share the relevant data of the manuscript “Stability limitations of optical frequency transfer in telecommunication DWDM networks”.</p> <p>The files:</p> <ul> <li>“fig_2a_total.txt”</li> <li>“fig_2a_diff.txt”</li> </ul> <p>contains respectively the timeseries data of phase fluctuations of total delay and differential delay between two fibers in 50 km-long soil-deployed cable. Same results for 110 km-long aerial cable are in files:</p> <ul> <li>“fig_2b_total.txt”</li> <li>“fig_2b_diff.txt”</li> </ul> <p>The files:</p> <ul> <li>“fig_3_aerial_MDEV_points.txt”</li> <li>“fig_3_soil_MDEV_points.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the differential delay fluctuations in 110 km-long aerial and 70 km-long soil-deployed cable. For short averaging the stability is limited by the noise of time interval counter (MTC 108). Before MDEV calculations the differential delay fluctuations was divided by factor of two, as a half of these fluctuations is present at the output of a stabilized system.</p> <p>The files:</p> <ul> <li>“fig_4_soil_MDEV_points_CW-total.txt”</li> <li>“fig_4_soil_MDEV_points_CW-diff.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the total and differential delay fluctuations in 70km soil-deployed cable, measured with the unmodulated coherent optical carrier.</p> <p>The files:</p> <ul> <li>“fig_7a_EDFA_phase.txt” and “fig_7a_EDFA_temp.txt”</li> <li>“fig_7b_Raman_phase.txt” and “fig_7b_Raman_temp.txt”</li> <li>“fig_7c_ROADM_phase.txt” and “fig_7c_ROADM_temp.txt”</li> </ul> <p>contains the timeseries data of simultaneous measurement of phase and temperature changes in individual cards (modules) of the DWDM system: EDFA amplifier, card containing the Raman module, as well as ROADM (Reconfigurable Optical Add Drop Multiplexer) module.</p> <p>Time series data of the fluctuations of the total and differential propagation delay of the signal transfer in the unmodified DWDM system (over 1500 km long route) include files:</p> <ul> <li>“fig_9_total_phase_fluct_part1”, “fig_9_total_phase_fluct_part2”, “fig_9_total_phase_fluct_part1” </li> <li>“fig_9_diff_phase_fluct_part1”, “fig_9_diff_phase_fluct_part2”, “fig_9_diff_phase_fluct_part3”</li> </ul> <p>The files:</p> <ul> <li>“fig_10_mdev_acusticNOISE.txt”</li> <li>“fig_10_mdev_air-conditioning_ON.txt”</li> <li>“fig_10_mdev_air-conditioning_MODIFIED.txt”</li> </ul> <p>contains points of modify Allan deviation that shows respectively: the influence of acoustic noise, stability of the frequency transfer in DWDM network in the case of sub-optimal (asymmetric) cooling within a single ILA node, while the last file shows the stability of the transfer after optimizing the airflow within this node.</p> <ul> </ul>
Training Datasets of Exhaust Temperature Model (ETM) and Three-Way Catalyst Control Lambda Set Point (TWCC) Model for Black-Box Modeling
<p>These are the two training datasets for the Exhaust Temperature Model (ETM) and the Three-Way Catalyst Control Lambda Set Point (TWCC) Model, used in Black-Box Modeling, such as through Neural Networks.</p> <p>The datasets for each black box model are stored in XLSX files. Each dataset file contains ECU channel inputs, related Map parameters, and output. The ECU channel inputs and Map parameters are collected with Sobol methods. The output is generated by Hardware-in-the-Loop (HiL) corresponding to the related ECU channel inputs and Map parameters.</p> <p>The ETM model has four effective Maps, hence there are 32 corresponding shape-based algorithm parameters. Its output is the exhaust temperature. Its ECU channel inputs are as follows:</p> <p>CH_1: Intake valve cut-off condition</p> <p>CH_2: Thrust cut-off condition</p> <p>CH_3: Cylinder equalization control work cycle injection enabled</p> <p>CH_4: Ignition angle efficiency</p> <p>CH_5: Relative level of reduction</p> <p>CH_6: Lean engine lambda for cylinders on exhaust bank with lambda split</p> <p>CH_7: Rich engine lambda for cylinders on exhaust bank with lambda split</p> <p>CH_8: Exhaust gas mass flow</p> <p>CH_9: Engine speed</p> <p>CH_10: Relative air filling</p> <p>CH_11: Material temperature</p> <p>CH_12: Engine temperature</p> <p> </p> <p>The TWCC model has one Map, hence there are 8 corresponding shape-based algorithm parameters. Its output is lambda set point. Its ECU channel inputs are as follows:</p> <p>CH_1: Exhaust mass flow</p> <p>CH_2: Trigger bit for inhibiting the purge</p> <p>CH_3: Trigger bit for a rich mixture</p> <p>CH_4: Trigger bit for empty catalyst</p> <p>CH_5: Trigger bit for catalyst reset</p> <p>CH_6: Oxygen storage level</p> <p>CH_7: Desired air-fuel ratio</p>
Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"
<p>Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"</p>
Data set for Typology of Standard Negation in South Asia
<p>The R script contains code to reproduce all figures. It also generates the binary data out of the data set for all languages. The languages.txt file contains data for all languages in the shape illustrated in Table 4 (with additional information on the languages) in the thesis. The binary_data.txt is left empty on purpose since the script fills it out.</p> <p>The R script requires two files in the same directory: the empty binary_data.txt sheet and the filled out languages.txt sheet.</p> <p>The former is a blank which will be filled out within R. The latter is basis for most of the commands and will be supplemented with additional information, again, within R. Thus, some of the information is not stored in the .txt files but accessible after running the script.</p>
integration of two scRNA-seq data sets covering 15 human liver donors (Ramachandran, 2019 Nature and Nkongolo, 2023 J Clin Invest)
<p>rds file for R software, containing a seurat object. Load with after calling Seurat library. https://satijalab.org/seurat/</p> <p>Original data matrices with raw readcounts were downloaded from NCBI GEO</p>
NMR data set from human and mice serum for ketone bodies evaluation
<p>This dataset contains the raw data from nuclear magnetic resonance analysis of serum samples from human and mice subjects. </p> <p> </p>
Data set for the article 'Electroreduction of CO to 2.8 A cm-2 C2+ Products: Maximizing Efficiency with Minimalist Electrode Design Featuring a Mesopore-Rich Hydrophobic Copper Catalyst Layer'
<p>This depository contains the data set for the article 'Electroreduction of CO to 2.8 A cm-2 C2+ Products: Maximizing Efficiency with Minimalist Electrode Design Featuring a Mesopore-Rich Hydrophobic Copper Catalyst Layer' (<a href="https://doi.org/10.1002/advs.202405938">https://doi.org/10.1002/advs.202405938</a>)</p>
Metabolomics data set for the project EHP-BFNU-OVNKM-4-139-2024
Open the record for dataset details and reuse information.
Data set for Sinhala Textbook Summarization (Grade 6)
<p>This data set was derived to fine-tune GPT-3 models, to support auto-summarization in Sinhala.</p> <p> </p> <p>The attached datasets were validated by School experts (teachers with more than 10 years of teaching experience in the Sinhala Language for grade 6 students)</p>
Data set related to article "PBX1-directed stem cell transcriptional program drives tumor progression in myeloproliferative neoplasm"
<p>This record contains raw data related to article "PBX1-directed stem cell transcriptional program drives tumor progression in myeloproliferative neoplasm"</p> <p>PBX1 regulates the balance between self-renewal and differentiation of hematopoietic stem cells and maintains proto-oncogenic transcriptional pathways in early progenitors. Its increased expression was found in myeloproliferative neoplasm (MPN) patients bearing the JAK2V617F mutation. To investigate if PBX1 contributes to MPN, and to explore its potential as therapeutic target, we generated the JP mouse strain, in which the human JAK2 mutation is induced in the absence of PBX1. Typical MPN features, such as thrombocythemia and granulocytosis, did not develop without PBX1, while erythrocytosis, initially displayed by JP mice, gradually resolved over time; splenic myeloid metaplasia and in vitro cytokine independent growth were absent upon PBX1 inactivation. The aberrant transcriptome in stem/progenitor cells from the MPN model was reverted by the absence of PBX1, demonstrating that PBX1 controls part of the molecular pathways deregulated by the JAK2V617F mutation. Modulation of the PBX1-driven transcriptional program might represent a novel therapeutic approach.</p>
Ecotoxicity data realted to the ELECTRA 826244 H2020 project Deliverable 2.1 entitled "Description of the prototypes set-up for field testing"
<p>Ecotoxicity testing was carried out within the frame of the ELECTRA No. 826244 H2020 project. Samples were provided by partners from laboratory experiments. This dataset contains ecotoxicity data related to technologies described in Deliverable 2.1 entitled Description of the prototypes set-up for field testing.</p>
Duke Spleen Data Set
<p>CT and MRI images with associated binary spleen segmentation masks; includes axial and coronal images, MRI includes SSFSE and T1w opposed phase contrasts; includes a range of normal and abnormal spleen sizes and shapes in the setting of chronic liver disease.</p>
Data Set: In-session dropout prediction model
<p>In-session dropout prediction model</p> <p>This project describes an in-session prediction model that predicts student early dropout from online learning exercises.<br> Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in<br> the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,<br> school homework and assignments are supposed to be completed by all learners. In the pandemic, online<br> learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to<br> transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting<br> learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the<br> online language learning platform orthografietrainer.net. We calculated time-progressive machine learning<br> models that predict dropout after each step (completed sentence) in the assignment using learning process<br> data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention<br> strategy that distinguishes between motivational and subject-specific interventions. <br> A random state is not set, thus, results might differ marginally.</p> <p>Whole project described in: <br> N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart<br> Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios<br> Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,<br> SciTePress, 2022, ISBN 978-989-758-562-3 </p> <p> </p> <p> </p>
Data Set of an online controlled experiment to study adaptive learning
<p>Online-controlled experiment evaluation - Data Set</p> <p>Digital learning platforms are more and more used in blended classroom scenarios in Germany. However, as learning processes are different among students, adaptive learning platforms can offer personalized learning, e.g. by individual feedback and corrections, task sequencing, or recommendations. As digital learning platforms are already used in classroom settings, we propose the transformation of these plat-forms into adaptive learning environments. To measure the effectiveness and improvements achieved through the adaptions an online-controlled experiment design is created. In our experiment, we therefore investigate the effectiveness of different inter-ventions on a large user group in a four-month online-controlled experiment. For this purpose, the highly frequented German learning platform Orthografietrainer.net was transformed into an adaptive learning platform and users were randomly assigned to different interventions.</p> <p>The experimental design is published here: N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart An Online Controlled Experiment Design to Support the Transformation of Digital Learning towards Adaptive Learning Platforms Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,, SciTePress, 2022, ISBN 978-989-758-562-3 </p> <p>The architectural concept is published here: Rzepka, N., Simbeck, K., Müller, H.-G. & Pinkwart, N., (2022). Adaptive Learning as a Service – A concept to extend digital learning platforms?. In: Henning, P. A., Striewe, M.-0. 0. & Wölfel, M.-0. 0. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 237-238). DOI: 10.18420/delfi2022-049 </p> <p>The findings of this experiment are published here: tba</p> <p>The code to this evaluation can be found on Zenodo: <a href="https://doi.org/10.5281/zenodo.7755546">10.5281/zenodo.7755546</a></p>
Data Set: Solution Probability in Online Learning Environments
<pre>Solution Probability Model and Fairness Evaluation This in-session prediction model seeks to predict the users’ performance on the Orthografietrainer.net platform. The target variable is binary and predicts if the user will do the following sentence correctly or not. For fairness evaluations the best models (MLP and DTE), and the worst model (SVM) are considered. A random state is not set, thus, results might differ marginally. A detailed description of the solution probability model and the fairness evaluation can be found here: tba</pre>
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.