Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

Data sets - The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities

Data sets  The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities.  In the period from February to October 2024, a survey of 112 computer science teachers in Kazakhstan (Pavlodar region) was conducted to determine attitudes to inclusive education, motivation to teach, and perception of the possible impact of computer science on students with mental disabilities. Questionnaire  https://docs.google.com/document/d/1LzukKSqW_mHMZXbMtN0ecmmU4cKJiwgf0laTWBHQSng/edit?usp=sharing This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP14872400).

Data set: The Effect of Chlorides on the Performance of DME / Mg[B(hfip)4]2 Solutions for Rechargeable Mg Batteries

Dataset of the continuum simulations generated and used within the paper "The Effect of Chlorides on the Performance of DME / Mg[B(hfip)4]2 Solutions for Rechargeable Mg Batteries", published in Journal of The Electrochemical Society (2023, 170 (9), 090542, DOI: 10.1149/1945-7111/acf960). One of the major issues in developing electrolyte solutions for rechargeable magnesium batteries is understanding the positive effect of chloride anions on Mg deposition-dissolution processes on the anode side, as well as intercalation-deintercalation of Mg2+ ions on the cathode side. Our previous results suggested that Cl– ions are adsorbed on the surface of Mg anodes and Chevrel phase MgxMo6S8 cathodes. This creates a surface add-layer that reduces the activation energy for the interfacial Mg ions transportation and related charge transfer, as well as promotes the transport of Mg2+ from the solution phase to the Mg anode surface and into the cathodes’ host materials. Here, this work further examines the effect of adding chlorides to the state-of-the-art Mg[B(hfip)4]2 / DME electrolyte solution, specifically focusing on reversible magnesium deposition, as well as the performance of Mg cells with benchmark Chevrel phase cathodes. It was observed that the presence of chlorides in these solutions facilitates both Mg deposition, and Mg2+ ions intercalation, whereby this effect is more pronounced as the purity levels of the solution is lowered.

Data set: Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance

Dataset of the continuum simulations generated and used within the paper "Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance", published in ChemSusChem (2021, 14 (21), 4820-4835, DOI: 10.1002/cssc.202101498). The performance of rechargeable magnesium batteries is strongly dependent on the choice of electrolyte. The desolvation of multivalent cations usually goes along with high energy barriers, which can have a crucial impact on the plating reaction. This can lead to significantly higher overpotentials for magnesium deposition compared to magnesium dissolution. In this work we combine experimental measurements with DFT calculations and continuum modeling to analyze magnesium deposition in various solvents. Jointly, these methods provide a better understanding of the electrode reactions and especially the magnesium deposition mechanism. Thereby, a kinetic model for electrochemical reactions at metal electrodes is developed, which explicitly couples desolvation to electron transfer and, furthermore, qualitatively takes into account effects of the electrochemical double layer. The influence of different solvents on the battery performance is studied for the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate electrolyte salt. It becomes apparent that not necessarily a whole solvent molecule must be stripped from the solvated magnesium cation before the first reduction step can take place. For magnesium reduction it seems to be sufficient to have one coordination site available, so that the magnesium cation is able to get closer to the electrode surface. Thereby, the initial desolvation of the magnesium cation determines the deposition reaction for mono-, tri- and tetraglyme, whereas the influence of the desolvation on the plating reaction is minor for diglyme and tetrahydrofuran. Overall, we can give a clear recommendation for diglyme to be applied as solvent in magnesium electrolytes.

Data set: Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance

Dataset of the continuum simulations generated and used within the paper "Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance", published in ChemSusChem (2020, 13 (14), 3599-3604, DOI: 10.1002/cssc.202001034). The choice of electrolyte has a crucial influence on the performance of rechargeable magnesium batteries. In multivalent electrolytes an agglomeration of ions to pairs or bigger clusters may affect the transport in the electrolyte and the reaction at the electrodes. In this work the formation of clusters is included in a general model for magnesium batteries. In this model, the effect of cluster formation on transport, thermodynamics and kinetics is consistently taken into account. The model is used to analyze the effect of ion clustering in magnesium tetrakis(hexafluoroisopropyloxy)borate in dimethoxyethane as electrolyte. It becomes apparent that ion agglomeration is able to explain experimentally observed phenomena at high salt concentrations.

Data set_WP5_T5.4_MOST

The flexible polyurethane foam was glycolyzed with ionic liquid (IL) and by employing separa?on techniques, the product was separated into polyols, IL and solid precipitate. The separated products were characterized by FTIR.

opencc-by-4.0Jun 2021View details →

Data set: Huai et al. (2021). JAMC. Quantifying rainfall in Greenland: a combined observational and modelling approach

Abstract Paper. This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in-situ precipitation gauge measurements to seven different precipitation phase schemes to separate rain- and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a Dynamic Correction Model (DCM) for Automatic Weather Stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R=0.3-0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02-1.40) are smaller than those for snowfall (1.27-2.80), and hence the observations more robust. With a horizontal resolution of 5.5 km and simulation period from 1958-present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients. The dataset contains: Automatic weather station data: AWS-daily.zip: per station daily values of snowfall and rain fall derived from raw precipitation data and for 7 methods to divide between rain and snowfall AWS-factork.zip: per station the factor with which the data is corrected for undercatch AWS-script.zip: the scripts used for the analyses Staffed weather stations: Meteo-daily.zip: per station daily values of snowfall and rain fall derived from the precipitation data and for 7 methods to divide between rain and snowfall Meteo-factork.zip: per station the factor with which the data is corrected for undercatch Meteo-script.zip: the scripts used for the analyses

opencc-by-4.0Jun 2021View details →

Data set: Forest management to increase carbon sequestration in boreal Pinus sylvestris forests

Data supporting the results and analyses published in Plant and Soil, "Forest management to increase carbon sequestration in boreal Pinus sylvestris forests". Data from a long-term fertilization (N and N+P) and thinning experiment in Pinus sylvestris stands across Sweden (56–67°N). Carbon stocks in soil and trees, tree growth, soil respiration and soil available nitrogen (ammonium, nitrate) are included. Data (data file + meta data file) include: jorgensen_etal_plantsoil_treesoil_data.csv (site data, carbon stocks: trees and their separate parts and soil, soil available nitrogen) jorgensen_etal_plantsoil_treesoil_data_METADATA.csv jorgensen_etal_plantsoil_resp_data.csv (site data, soil respiration, temperature, moisture) jorgensen_etal_plantsoil_resp_data_METADATA.csv R-script: Jorgensen_etal_plantsoil.R

opencc-by-4.0Jun 2021View details →

Polarized Sensory Positioning, eight data sets

Eight different datasets using Polarized Sensory Positioning (PSP) for evaluation of food/drink samples. The different datasets are given in separate sheets of the file. The datasets are further described in sheet "INFO" and samples for different datasets are described with the data.  Useful for comparison of data analytical methods.

Data set: Can Stephen Curry really know? - Conscious access to outcome prediction of motor actions

Data set associated with the following pre-print: Can Stephen Curry really know? - Conscious access to outcome prediction of motor actions. bioRxiv: 2021.03.30.437477

Original data sets of a DEER/PELDOR ring test of four doubly spin-labelled mutants of the protein YopO

The dataset is discussed in manuscript "Benchmark test and guidelines for DEER/PELDOR experiments on nitroxide-labeled biomolecules" by Olav Schiemann, Caspar A. Heubach, Dinar Abdullin, Katrin Ackermann, Mykhailo Azarkh, Elena Bagryanskaya, Malte Drescher, Burkhard Endeward, Jack H. Freed, Laura Galazzo, Daniella Goldfarb, Tobias Hett, Laura Esteban Hofer, Luis Fábregas Ibáñez, Eric J. Hustedt, Svetlana Kucher, Ilya Kuprov, Janet E. Lovett, Andreas Meyer, Sharon Ruthstein, Sunil Saxena, Stefan Stoll, Christiane Timmel, Marilena Di Valentin, Hassane S. Mchaourab, Thomas F. Prisner, Bela E. Bode, Enrica Bordignon, Marina Bennati, Gunnar Jeschke. It was generated in a ring test by seven laboratories. The authors names of individual data are not assigned on purpose, rather data sets are referred to by laboratory identifiers A, B, C, D, E, F, G. The Supplementary Information for the above mentioned manuscript, which contains a link to this dataset, describes how the samples were prepared and how the measurements were performed.

Data set for AYUSH interventions for COVID-19- A Living Systematic Review and Meta-analysis

The COVID-19 pandemic has put a huge strain on governments and medical professionals all across the world. To identify acceptable treatments, many clinical studies from the Indian system of Traditional Medicines [Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homoeopathy (AYUSH)] have been conducted. Objective of the study is determine the efficiency of the Traditional System of Indian Medicine (AYUSH system) in lowering the incidence, duration, and severity of COVID-19 through a living systematic review and meta-analysis. We will search the following databases e.g; Pubmed; the Cochrane central register of controlled trials (CENTRAL); the Clinical Trials Registry - India (CTRI); Digital Helpline for Ayurveda Research Articles (DHARA): AYUSH research portal; WHO COVID-19 database etc. Clinical improvement, WHO ordinal scale, viral clearance, incidences of COVID-19 infection, and mortality will be considered as primary outcomes. Secondary outcomes will be use of O2 therapy or mechanical ventilator, admission to high dependency unit or emergency unit, duration of hospitalization, the time to symptom resolution, and adverse events. The review will be updated bi-monthly with two updates. It will provide practitioners, guideline developers, and authorities with up-to-date syntheses on interventions on a regular basis to help them make health-care decisions about AYUSH therapies for COVID-19 management. Study is supported by World Health Organization, South East Asia Regional Office, New Delhi, India.  Here, we shared the result of our search strategy of our project and data extraction tool developed.

opencc-by-4.0Oct 2020View details →

A new lepto-hadronic model applied to the first simultaneous multiwavelength data set for Cygnus X–1

This is a basic reproduction package for the paper "A new lepto-hadronic model applied to the first simultaneous multiwavelength data set for Cygnus X-1" by [Kantzas et al. (2020)](<a href="https://doi.org/10.1093/mnras/staa3349">https://doi.org/10.1093/mnras/staa3349</a>).

Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper

Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper

Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis

We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available. Please cite:  https://pubmed.ncbi.nlm.nih.gov/24521231/  https://pubs.acs.org/doi/10.1021/ci400709d

Perceived stress and e-learning readiness data set during COVID-19

The COVID-19 pandemic has restricted all educational institutions from the traditional campus-based education system to e-learning worldwide. However, adapting to this new platform, e-learning readiness may cause perceived stress among students. This study aimed to examine the association between e-learning readiness and perceived stress as well as the relationship with socio-demographic and e-learning related factors. This cross-sectional study was employed, where 1145 e-learning enrolled university students were surveyed.

Data Sets and Results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins"

Data sets and results for "Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins" The file "dna_binding_protein_sequences.zip" has the training and testing sets from the paper: RLL - "random_<train/test>_full_1000.csv" RSL - "random_<train/test>_40.csv" RS&LL - "random_<train/test>_40_1000.csv" RLL where included positive examples have verified DNA binding activity - "random_<train/test>_hq_1000.csv" The 10 RS&LL data sets - "random_<train/test>_40_1000.csv" + "random_<train/test>_40_1000_cv_<0-8>.csv" The results files are named similarly. See "see_results.ipynb" in the <a href="https://doi.org/10.5281/zenodo.5153683">codebase</a> that supplement these data sets The species data sets are derived from "uniprot_data_bac.tab" and "uniprot_data_not_bac.tab."  See <a href="https://doi.org/10.5281/zenodo.5153683">code</a>.  The ESM embeddings used by the XGBoost model are in "dna_binding_protein_esm.zip"

Training and test data set of thickness distribution of turbidites

This is a data set of thickness distribution used for training and test of the inverse model of turbidites. Details were described in https://esurf.copernicus.org/preprints/esurf-2020-93/esurf-2020-93.pdf

Data set: Genome scale CRISPR Cas9a knockout screen reveals genes that controls glioblastoma susceptibility to the alkylating agent temozolomide (TMZ).

Glioblastoma is the deadliest of all primary brain tumor with a very poor survival outcome. Alkylating agent such as temozolomide is used as a mainstay drug used in treating of glioblastoma patients including radiation, tumor treating field and surgery. However, this drug offers little to no benefit for the glioblastoma patients. Hence, the objective of the generation of this data is to understand and unravel genes that controls  glioblastoma susceptibility and resistance to temozolomide using an unbiased genome scale CRISPR Cas9a knockout screen

PGA Tour Data Set - 2018 to 2021

Data Set for PGA Tour Results for the 2018 to 2021 seasons. Additional incorporation of Player Profile data, Weather data and Golf Course Data