Find research datasets worth reusing

opencc-by-4.0Jun 2019View details →

Multicrystal data of proteinase K collected on the VMXi beamline at the Diamond Light Source, UK

These are a series of datasets which have been collected on the VMXi beamline at the Diamond Light Source, UK. They were collected in-situ at room temperature using a Dectris 2X 4M detector. The datasets were collected in unattended mode using samples preselected using the SynchWeb interface to ISPyB. Each dataset is a 60 degree wedge of data, collected using 1% DMM (double multilayer monochromator) beam (at a wavelength of 0.979A) with an exposure time of 0.002 secs/ frame. These data were merged and have been used in the deposition of a structure to the protein databank.

opencc-by-sa-4.0Aug 2019View details →

Source data belonging to "Visualisation of dCas9 target search in vivo using an open-microscopy framework"

Source data corresponding to "Visualisation of dCas9 target search in vivo using an open-microscopy framework". Contains  pTarget and pNonTarget raw datasets, as well as all localization data, cell UV intensity data, cell outline data, and analysed diffusion coefficient lists.

opencc-by-4.0Apr 2019View details →

Details of offspring and source data for analysis of metabolic health and dietary preference in a rat model of acute alcohol exposure.

This Excel file contains information on the number of offspring used to examine each outcome and the raw data for each data Table and Figure within a manuscript submitted to Journal of Physiology.

opencc-by-4.0Sep 2019View details →

Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet"

Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet" by I. Belopolski et al., Science 365, 1278-1281 (2019).

opencc-by-4.0Oct 2019View details →

Multi-Source Distributed System Data for AI-powered Analytics

Abstract: In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems. The major contributions have been materialized in the form of novel algorithms. Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms. Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.   Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research. Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation. General Information: This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset. You may find details of this dataset from the original paper: Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".  If you use the data, implementation, or any details of the paper, please cite!   BIBTEX: _________________________________________ <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> ___________________________ The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests. The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format. Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data. Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a>

opencc-by-4.0Oct 2014View details →

Text-fig. 5. Size comparison of lion p4 and m1 from Za Hájovnou Cave with close relative forms from European sites (black: Panthera fossilis, grey: Panthera cf. fossilis or Panthera fossilis – spelaea; f = female, old c. = old collection). Data source: Wojtusiak 1953, Thenius 1972, Schütt and Hemmer 1978, Argant 1988, 1991, García 2003, Baryshnikov and Tsoukala 2010). in Panthera Fossilis (Reichenau, 1906) (Felidae, Carnivora) From Za Hájovnou Cave (Moravia, The Czech Republic): A Fossil Record From 1987-2007

Text-fig. 5. Size comparison of lion p4 and m1 from Za Hájovnou Cave with close relative forms from European sites (black: Panthera fossilis, grey: Panthera cf. fossilis or Panthera fossilis – spelaea; f = female, old c. = old collection). Data source: Wojtusiak 1953, Thenius 1972, Schütt and Hemmer 1978, Argant 1988, 1991, García 2003, Baryshnikov and Tsoukala 2010).

opencc-by-4.0Jul 2024View details →

Source data for the kinetic assays in publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase"

Datasets of enzyme kinetics related to the publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase", published in Nature Communications with DOI: https://doi.org/10.1038/s41467-024-50933-6  Individual files contain raw kinetic reaction data of the mycobacterial IMPDHs (wild type and mutant forms Mycobacterium smegmatis or wild type Mycobacterium tuberculosis) as a function of IMP, NAD+, GTP, ATP, ppGpp and Mg2+ concentration. Individual data sets are presented as time data points of the absorbance at 340 nm in an Excel file with a linked Graphpad graphical link. Detailed experimental conditions are available in the related publication.

opencc-by-4.0Jul 2024View details →

Source data for the HDX-MS experiments in publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase"

Dataset of HDX-MS experiments related to the publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase", published in Nature Communications with DOI: https://doi.org/10.1038/s41467-024-50933-6  The differential HDX-MS experiments compare the apo and ligand-bound states of IMPDH from Mycobacterium smegmatis. A description of the dataset is provided in the attached README file: IMPDH_HDX-MS_README.txt. Detailed experimental conditions are available in the related publication.

Codes and source data for "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies"

This repository contains the MATLAB (R2022b) codes (*.m files) and the figure source data (.mat files)  for the paper "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies" (He et al., 2024).  For installation of MatLab, please refer to: https://au.mathworks.com/products/matlab.html For queries about this repository and its contents, please contact Dr. Qingyou He (qyhe@scsio.ac.cn). %% Fig1.m: For plotting main Fig.1. %% Fig2.m: For plotting main Fig.2. %% Fig3.m: For plotting main Fig.3. %% Fig4.m: For plotting main Fig.4. %% Fig5.m: For plotting main Fig.5. %% Fig6.m: For plotting main Fig.6. %% Data Fig1.mat: For main Fig.1. %% Data Fig2.mat: For main Fig.2. %% Data Fig3.mat: For main Fig.3. %% Data Fig4.mat: For main Fig.4. %% Data Fig5.mat: For main Fig.5. %% Data Fig6.mat: For main Fig.6. References: He, Q., W. Zhan, M. Feng, Y. Gong, S. Cai, and H. Zhan (2024), Common occurrences of subsurface heatwaves and cold spells in ocean eddies, Nature, 634, 1111–1117, doi:10.1038/s41586-024-08051-2.

opencc-by-4.0Jul 2024View details →

Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches

The primary goal of this dataset is to enhance the spatial and temporal distribution of emissions from civil aviation (NFR1.A.3.a), road transport (NFR1.A.3.b), railways (NFR1.A.3.c), and military aviation (NFR1.A.5), using Portugal as case study. For more information, please refer to the published article “Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches” (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.102097" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.uclim.2024.102097</a>). This dataset contains the following folders and files: 1. Spatial_Location  1.1. NFR1_A_3_a.gdb: Geodatabase containing the locations of Portuguese airports and aerodromes.  1.2. NFR1_A_3_b.gdb: Geodatabase containing the locations of Portuguese roads.  1.3 NFR1_A_3_c.gdb: Geodatabase containing non-electrified Portuguese railways and train station locations.  1.4 NFR1_A_5.gdb: Geodatabase containing the locations of Portuguese military airport facilities. 2. Temporal_Profiles  2.1. Daily   2.1.1. Daily_NFR1_A_3_a.csv: This csv file contains the daily movements profiles of civil aviation sites in Portugal.  2.2. Hourly   2.2.1. Hourly_NFR1_A_3_b.txt: This txt file contains the hourly road traffic volume profiles for the road transport activities in Portugal at different locations (BigAir column).   2.2.2. Hourly_NFR1_A_3_c.txt: This text file contains the hourly railway profile in Portugal, categorized by line and train station. 3. Emission_Factors  3.1. EF_NFR1_A_3_a.xlsx: This Excel file contains emission factors for civil aviation activities, categorized by technology, flight phase, fuel, and pollutant. Additionally, it includes information about engines and aircraft.  3.2. EF_NFR1_A_3_b.xlsx: This Excel file contains emission factors for road transport activities, categorized by vehicle type, technology, fuel, abatement, and pollutant. Emission factors for road resuspension are not provided because the papers using this dataset are still under review.  3.3. EF_NFR1_A_3_c.xlsx: This Excel file contains emission factors for railways activities, categorized by technology, fuel, and pollutant.  3.4. EF_NFR1_A_5.xlsx: This Excel file contains emission factors for military aviation activities, categorized by fuel, and pollutant. 4. Other_Info  4.1 NFR1_A_3_b: This folder contains information organized by road segments, including fuel consumption (in the “FuelConsumption” folder), hourly meteorology (in the “Meteorology” folder), population data (in the “Population” folder), daily traffic volume (in the “TrafficVolume” folder), vehicle categories (in the “VehicleCategory” folder), and vehicle classes (in the “VehicleClasses” folder). Additionally, it includes the link between road traffic volume measurement points and the Portuguese road network (in the “sensorsVSroads” folder)

SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data

<div> SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data. </div>

Source Data for the paper: "Quantum-classical simulations reveal the photoisomerization mechanism of a prototypical first-generation molecular motor"

This dataset contains the raw data for the results shown in the paper. For each figure of the paper (main text), one directory with data file(s) is provided.

Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")

This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (https://doi.org/10.1016/j.jhydrol.2024.131930). Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.

data source for paper "Unity of granular configuration of soil"

<ol> <li>'Table S1' presents soil data sources from China, around the world, and from lunar samples, including their abbreviations, soil textures, locations, and specimen numbers.</li> <li>'GSD data S2' contains grain size distribution data for soils belonging to four models: unimodal, bimodal, trimodal and multimodal.</li> <li>'Matlab code for simulating soil grain size data S3' is designed to simulate soil particles from the source area.</li> </ol>

Data from public granaries as a source of proxy data on grain harvests and weather extremes (Sušice region, Czech Republic)

This deposit contains eight .xlsx files related to the study of public granary data and their relation to grain harvests and weather extremes in the Su&scaron;ice region (Czech Republic). In the study, annual values of grain borrowed by serfs, their grain depositions, the total grain storage and the total debt of serfs at the end of year were used to calculation of weighted grain indices considering the balance between borrowed and returned grain: a weighted bad harvest index (WBHI), a weighted good harvest index (WGHI), a weighted stored grain index (WSGI: WSGI-, more borrowed than returned; WSGI+, more returned than borrowed) and a weighted serf debt index (WSDI: WSDI+, more borrowed than returned grain; WSDI-, more returned than borrowed grain). WBHI, WSGI- and WSDI+ were used to select years of extreme bad harvest and WGHI, WSGI+ and WSDI- years of extreme good harvest. Selected extreme harvest years were tested against documentary weather data and reconstructed temperature, precipitation and drought series of the Czech Lands. First six files (01a_borrowed grain_plus, 01b_returned grain_plus, 01c_stored grain_minus, 01d_stored grain_plus, 01e_serfs debt_plus, 01f_serfs debt_minus) contain three sheets representing individual cereals (rye, oats and barley). For each cereal, there are series of multiples of standard deviation used for calculation of individual indices mentioned above. These multiples were obtained from detrended series (using high-pass filter) of every grain characteristic as arithmetic mean plus/minus corresponding multiple of standard deviation. The file &bdquo; 02_indices“ contains three sheets with weighted grain indices: WBHI, WGHI, WSGI-, WSGI+, WSDI+, WSDI-. Sheets represent individual cereals – rye, oats and barley. The file &bdquo; 03_clima factors“ contains mean seasonal (DJF, MAM, JJA, and SON) temperature, precipitation and scPDSI for the period 1789–1849, expressed in deviations relative to the 1961–1990 reference period. The series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).

An Online Integrated Development Environment for Automated Programming Assessment Systems Open Source Data

This dataset accompanies the paper "An Online Integrated Development Environment for Automated Programming Assessment Systems". It contains data from the usability evaluation of a feature-rich online IDE designed for integration into Automated Programming Assessment Systems (APASs). The dataset includes survey responses from 27 participants based on the Technology Acceptance Model (TAM), performance metrics such as memory usage, and qualitative user feedback. The study highlights challenges in integrating online IDEs with APASs, such as memory efficiency, load balancing, and user experience. The dataset supports further research in developing scalable, effective, and user-friendly programming education tools. Here you can find the code changes required for the online IDE in Artemis: <a href="https://github.com/ls1intum/Artemis/pull/6706/files" target="_blank" rel="noopener">Github</a>

Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification

Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!". Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>. The file "nonlinear-poro-viscoelasticity.cc" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file. Funding: The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.

uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells

This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.

opencc-by-4.0Jun 2021View details →

Data for: A further source of Tokyo earthquakes and Pacific Ocean tsunamis

This data repository contains the location of cores collected as part of this study, the microfossil and radiocarbon results from those cores, and fault parameters used in eleven historical and hypothetical tsunami simulations for Kujukuri, Japan (Boso Peninsula). The work is supported by the Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST) and in part by grants awarded to J.E.P. [National Science Foundation (EAR-1303881 and 1624612), Natural Sciences and Engineering Council of Canada (NSERC), Canada Research Chair (CRC) program, and  Japan Society for the Promotion of Science (JSPS) International Research Fellow program at the Geological Survey of Japan (PE14038)]; A.C.P. [Science Foundation Ireland Career Development Award (17/CDA/4695),  Investigator Award (16/IA/4520), Marine Research Programme funded by the Irish Government, co-financed by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01), European Union’s Horizon 2020 research and innovation programme under grant agreement No 818144, and SFI Research Centre (16/RC/3872 and 12/RC/2289_P2); and B.P.H. [Singapore Ministry of Education Academic Research Fund (MOE2019-T3-1-004), National Research Foundation Singapore, and Singapore Ministry of Education, under the Reseach Centers of Excellence initiative].