Find research datasets worth reusing

Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"

This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)

Two data sets of gravitational field degree-2 order-1 Stokes coefficient

The SLR Stokes coefficient data are provided by the Space Research Institute (IWF) of the Austrian Academy of Sciences based on SLR observations coordinated by ILRS. The GRACE satellite data are from the Center for Space Research RL06 solutions. All    ocean tides, Earth tides and polar tides have been removed.

Data set for EMPIR project MeDDII paper bmt-2022-0039

Data set for EMPIR project MeDDII paper https://doi.org/10.1515/bmt-2022-0039 In-line measurements of the physical and thermodynamic properties of single and multicomponent liquids

Mode structure reconstruction by detected and undetected light: data sets

Data sets related to the multimode optical field reconstructions reported in manuscript <a href="https://arxiv.org/abs/2212.13873v1">arXiv:2212.13873v1</a>

Data set on district heating potentials in EU-27 countries

The provided data set includes the input data for assessment of the district heating potential in EU-27 countries under evolving DH market shares and ambitious heat demand reduction scenario. The data set also includes the output data of the assessment obtained based on the input data from the following sources: <ul> <li>Best-Case scenario from: <ul> <li>"L. Kranzl et al., Renewable space heating under the revised Renewable Energy Directive: ENER/C1/2018 494 : final report. LU: Publications Office of the European Union, 2022 [Online]. Available: https://data.europa.eu/doi/10.2833/525486. [Accessed: Jul. 29, 2022]"</li> </ul> </li> <li>BL2050 scenario from sEEnergies project: <ul> <li>"B. Möller, E. Wiechers, L. Sánchez-García, and U. Persson, ‘Spatial models and spatial analytics results’, Mar. 2022, doi: 10.5281/zenodo.6524594. [Online]. Available: https://zenodo.org/record/6524594. [Accessed: Dec. 14, 2022]"</li> </ul> </li> </ul>

Rocking math at school with hypnosis: Effective reduction of math test anxiety with an audio hypnosis intervention in a school setting

Math as a school subject causes anxiety in many students. Previous results show that male and female students do not differ in their math performance. However, female students are more affected by math anxiety than male students, so that they might hesitate pursuing STEM careers. Actively addressing math anxiety requires an intervention that reduces anxiety, changes attitude, and provides positive experiences. Hypnosis interventions are particularly suitable for this. Therefore, we developed a 30-minutes audio-hypnosis intervention and tested its effectiveness in this study. We randomly assigned students into an intervention group (N = 29) and a control group (N = 38). All students performed two real math tests at school. The intervention group listened at least once to the hypnosis audio intervention between both math tests. Immediately before each math test, we assessed state-math anxiety (measured with STAI-S). To investigate performance differences, we analyzed students’ math performance scores in both math tests. Our results show that the hypnosis intervention led to a significant reduction in state-math anxiety, t(28) = 2.42, p = .022, Cohen`s dz = 0.45. Math performance did not differ significantly between math tests in the intervention group. Female students generally had higher trait math anxiety (as measured by SIMA scale) and showed a greater reduction in state math anxiety due to the hypnosis intervention, t(18) = 2.86, p = .010, Cohen`s dz = 0.66, compared to male students. It is possible that reduced state math anxiety could result in improved math performance in the long run. Overall, we conclude that our hypnosis intervention reduces math anxiety, especially in female students. Therefore, it is a promising method to promote especially females at an early point in their career and encourage them to choose STEM pathways. The final goal would be to establish hypnosis-based interventions at school encouraging students to activate their resources and develop their potential in all fields.

opencc-zeroJan 2023View details →

dryad36/100

Solidago hybrid-sequence capture probe set

Premise of the study: The phylogenetic relationships among the ca. 138 species of goldenrods (Solidago; Asteraceae) have been difficult to infer due to species richness, and shallow interspecific genetic divergences. This study aims to overcome these obstacles by combining extensive sampling of goldenrod herbarium specimens with the use of a custom Solidago hybrid-sequence capture probe set. Methods: A set of tissues from herbarium samples comprising ca. 90% of Solidago species was assembled, and DNA was extracted. A custom hybrid-sequence capture probe set was designed, and data from 854 nuclear regions were obtained and analyzed from 209 specimens. Maximum likelihood and coalescent approaches were used to estimate the genus phylogeny for 157 diploid samples. Key results: Although DNAs from older specimens were both more fragmented and produced fewer sequencing reads, there was no relationship between specimen age and our ability to obtain sufficient data at the target loci. The Solidago phylogeny was generally well supported, with 88/155 (57%) nodes receiving ≥95% bootstrap support. Solidago was supported as monophyletic, with Chrysoma pauciflosculosa identified as sister. A clade comprising Solidago ericameriodes, Solidago odora, and Solidago chapmanii was identified as the earliest diverging Solidago lineage. The previously segregated genera Brintonia and Oligoneuron were identified as placed well within Solidago. These and other phylogenetic results were used to establish four subgenera and fifteen sections within the genus. Conclusions: The combination of expansive herbarium sampling and hybrid-sequence capture data allowed us to quickly and rigorously establish the evolutionary relationships within this difficult, species-rich group.

Data Set: Balanced Magnetic Antenna for Partial Discharge Measurements in Gas-Insulated Substations

Data set for the publication named: Balanced Magnetic Antenna for Partial Discharge Measurements in Gas-Insulated Substations. Each header corresponds to the figure and legend.

Data set for "Electrical spectroscopy of defect states and their hybridization in monolayer MoS2"

Data set for "Electrical spectroscopy of defect states and their hybridization in monolayer MoS2" doi:10.1038/s41467-022-35651-1

FhuF, a ferric-siderophore reductase from E. coli K-12 - X-ray diffraction raw data set

This diffraction data set was collected at 100 K to 1.9 Å resolution at ALBA Synchrotron Beamline XALOC on December 5, 2021.

Data set and analytic codes supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia"

This deposit contains data set and analytic codes** (accompanied with a meta data) supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia". We investigated the impacts of land-use change on semi-aquatic bug (Gerromorpha, Hemiptera) communities in Sabah, Malaysia. Semi-aquatic bugs were collected from streams in old-growth forest, logged forest, and oil palm with and without riparian buffer strips. A range of environmental parameters were also collected to represent environmental conditions (associated with land-use change). Environmental data were collected at catchment, riparian, and stream scales, and were used separately for the assessments of their effects on the bugs. We looked at the effects on the abundance, biomass, species richness, and community composition of semi-aquatic bugs. We also assessed the effects on the proportion of juveniles, winged individuals, and female Ptilomera sp. (a morphospecies with clear sexual dimorphism in this study). This research was funded by the Jardine Foundation, the Cambridge Trust, the Natural Environment Research Council (NERC) (studentship 1122589), Proforest, the Varley Gradwell Travelling Fellowship, the Tim Whitmore Fund, the Panton Trust, the Cambridge University Commonwealth Fund, the Hanne and Torkel Weis-Fogh Fund, and the S.T. Lee Fund.   ** For reproducibility of outputs of the Canonical Correspondence Analysis (CCA), do insert the following function in the R Markdown before the line of "anova.cca(CCAEnvInsect, by = 'terms', first = TRUE)": set.seed(42) # About set.seed: <a href="https://stackoverflow.com/questions/13605271/reasons-for-using-the-set-seed-function">r - Reasons for using the set.seed function - Stack Overflow</a>

opencc-by-4.0Aug 2020View details →

COLO829 Somatic SV truth set

Data for generation of somatic SV truth set using COLO829 cell line. Includes the final truth set and also raw SV calling for each of the datasets, dataset-specific somatic SVs and copy number alterations. Updated with compliant VCFs

Mechanical behavior of C45 steel at high temperatures and high strain rates—experimental data set and numerical approach

In this publication we provide experimental data of dynamic compression tests of four microstructural variants of the C45 steel alloy performed at high temperatures and high strain rates. The presented data evidences the presence of Dynamic Strain Aging (DSA) in the material. Moreover, we provided the MATLAB codes of a calibration approach to estimate the material parameters of a plasticity model that accounts for DSA. The file "Mechanical behavior of C45" contains: 1) Folder EXP_DATA contains experimental data of dynamic compression tests of the C45 steel variants in .mat format. The data is organized in cell arrays, and every cell in the arrays contains the data of one experiment. The data of each experiment is arranged as four columns arrays, as: column 1: Plastic strain, column 2: Flow stress, column 3: absolute temperature, column 4: strain rate 2) Matlab code named CALIBRATION.m which executes a calibration approach of a modified Johnson-Cook that accounts for DSA. The form of the model is the following: \(\sigma(\varepsilon ,T,\dot{\varepsilon}) = (A+B\varepsilon^n)\left(1+Cln\left(\frac{\dot{\varepsilon}}{\dot{\varepsilon}^{ref}}\right)\right)\left( 1-\left(\frac{T-T^{ref}}{T^{melt}-T^{ref}}\right)^m\right) +\sigma^{dsa}(\varepsilon ,T,\dot{\varepsilon}) \) with, \(σ^{dsa} (ε,T,\dotε )=\frac{B_1}{ν} \left(\frac{ε^β}{ ε ̇T} exp⁡(-\frac{Q_m}{KT}) \right)^{\frac{2}{3}}\), \(\frac{B_1}{\nu} = \frac{\psi \dot{\varepsilon}}{exp(\eta \frac{T}{\dot{\varepsilon}^\alpha})} \)   The code CALIBRATION contains a data presentation section that can be used to generate plots to compare calibrated models with the experimental data. Further instructions are commented in the code CALIBRATION.m.

openMar 2022View details →

A Twitter Streaming Data Set collected before and after the Onset of the War between Russia and Ukraine in 2022

Social media can be mirrors of human interaction, society, and world events. Their reach enables the global dissemination of information in the shortest possible time and thus the individual participation of people all over the world in global events in almost real-time. However, equally efficient, these platforms can be misused in the context of information warfare in order to manipulate human perception and opinion formation. The outbreak of war between Russia and Ukraine on February 24, 2022, demonstrated this in a striking manner.  Here we publish a dataset of raw tweets collected by using the Twitter Streaming API in the context of the onset of the war which Russia started on Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia's invasion of Ukraine. We publish the IDs of all tweets we streamed during that time, the time we rehydrated them using Twitter's API as well as the result of the rehydration. If you use this dataset, please cite our related Paper:  <blockquote> Pohl, Janina Susanne and Seiler, Moritz Vinzent and Assenmacher, Dennis and Grimme, Christian, A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 (March 25, 2022). Available at SSRN: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543</a> </blockquote>

DATA SET HELBER MAURICIO SANDOVAL CUMBE

This data set it's test as part of an academic excersice conducted by the Cooperative University.

Data set: 6perfis+3prisioneiros3

Classes profiles and evaluation entries used in the article Multicriteria Classification of Reward Collaboration Proposals.

Informes de casos de estudio finales en hábitats humanos (anexo del libro: La visión sistémica del ambiente construido, 2024). [Data sets for the article: The Habitat Intervention Design Process -Part II]

From the book: Estos informes están escritos en español por los estudiantes que se mencionan en cada informe, quienes son los autores. Cada uno de estos seis documentos contiene casos de estudio y muestra la implementación de la metodología del 'Proceso de Diseño de Intervención del Hábitat' (IDP) en cada uno de los 6 casos mencionados en el libro (Capítulo 2.5 y Capítulo 3.2) (Libro: La visión sistémica del ambiente construido).  Estos hacen parte de la Fase 2 Fundamentos Prácticos del Modelo del proyecto de investigación "Modelo Pedagógico para la Enseñanza del Diseño de Intervención del Hábitat en Programas de Educación Superior".  From the article: These reports are Spanish-written. Each of these six documents contains one of the case studies and shows the implementation of the 'Habitat Intervention Design Process' (IDP) methodology in each of the 6 case studies mentioned in the article "The Habitat Intervention Design Process -Part II: A Transdisciplinary Model in the Pedagogy of the Design of the Built Environment" published in The International Journal of Design Education in its 2023 version. Find it here: <a href="https://doi.org/10.18848/2325-128X/CGP/v17i02/155-195">https://doi.org/10.18848/2325-128X/CGP/v17i02/155-195 </a>

Alaska Range Suture Zone Compiled Geochronology data set

Geochronology data set for the manuscript, Upper-plate Controls on Slab Geometry, Melt Ponding, and Structurally Compelled Localized Alaska Range Suture Zone Arc Magmatism Since ca. 100 Ma, submitted to Tectonics and seismicity of Alaska and Western Canada: Earthscope and beyond."

Heads-on skyrmion collisions data set

These are datafiles and code associated with the manuscript “Heads-on skyrmion collision”. The data files stored here consists on magnetic force microscopy (MFM) data (i.e. images) of a skyrmion hosting thin film. It is divided on folders corresponding to different days of measurement, and also divided by the type of MFM probe used. The format of the files is the proprietary format from NT-MDT. “Summary of collisions_V8” is a PowerPoint summarizing the data analysis carried out identifying the position of each skyrmion in each of the MFM data files. It should be used to understand the structure of the data. “main.m” Is the Matlab code used to help localizing the position of the skyrmions in the MFM images. “collisions.opju” is the OriginLab file containing the summary of the skyrmion positions. For questions about the dataset or the associated manuscript, please feel free to emai Héctor Corte-León at corte@nanosurf.com

Data Set for Predicting the Performance of ATL Model Transformations

Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained. Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models that were transformed. We provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Additionally, we calculated the mean absolute percentage error in % and the 95th percentile of the absolute percentage error in % for each experiment and provide these results. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models, the Java projects used to measure the execution time of the transformations, and other supporting scripts, e.g. for the analysis of the results. A short introduction with a quick start guide can be found in README.md and a detailed documentation in documentaion.pdf.