Find research datasets worth reusing

The appendix for evaluating the impact of large-scale nucleic acid testing and home quarantine on COVID-19 prevention and control: a dynamic modeling approach

Appendix I: The program of evaluating the impact of large-scale nucleic acid testing and home quarantine on COVID-19 prevention and control: a dynamic modeling approach Appendix II: The sensitivity analysis of evaluating the impact of large-scale nucleic acid testing and home quarantine on COVID-19 prevention and control: a dynamic modeling approach

Dataset for Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions

This is the dataset for the paper entitled "Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions" by Marciano et al.

Movie S6. Dynamics of the blood circulation in 54 hpf dock11-knockout zebrafish embryos

Representative confocal time lapses of the trunk region of a 54 hours postfertilization (hpf) dock11‑knockout fli1:GFP;gata1a:dsRed double transgenic. Endothelial cells from the intersegmental vessels are visualized in green, while blood cells are shown in red. This movie corresponds to Movie S6 from Supplementary Appendix of Block et al., Systemic Inflammation and Normocytic Anemia in DOCK11 Deficiency. N Engl J Med 2023.

Movie S5. Dynamics of the blood circulation in 54 hpf control zebrafish embryos

Representative confocal time lapses of the trunk region of a 54 hours postfertilization (hpf) control fli1:GFP;gata1a:dsRed double transgenic. Endothelial cells from the intersegmental vessels are visualized in green, while blood cells are shown in red. This movie corresponds to Movie S5 from Supplementary Appendix of Block et al., Systemic Inflammation and Normocytic Anemia in DOCK11 Deficiency. N Engl J Med 2023.

Surface tension coefficient of acqueous glycerol from Molecular Dynamics simulations

The dataset contains the configuration files and the results of molecular simulations of aqueous glycerol liquid slabs. The aim of the simulations is to determine the surface tension of the interface between aqueous glycerol and its vapour. Simulations are performed with Gromacs 2021. The water model is SPC/E, while the force field for Glycerol is extrapolated from OPLS-AA according to the work by Jahn et al. (<a href="https://doi.org/10.1021/jp5059098">doi.org/10.1021/jp5059098</a>). Surface tension is computed using Gromacs analysis tool by running <pre><code>gmx energy -f ener.edr</code></pre> and selecting the #Surf*SurfTen term, which returns the surface tension (bar*nm) times the number of surfaces (2, due to the system's periodicity). The surface tension is computed from the difference between interface-perpendicular and interface-parallel components of the pressure tensor (see Gromacs documentation or Frenkel and Smit Understanding Molecular Simulations second edition p.472). The nomenclature of the zip files indicates the mass fraction of glycerol, ranging from 0% (Glycerol000) to 100% (Glycerol100).

Molecular Dynamics simulations of acqueous glycerol spreading on a silica-like surface

This dataset contains the results of Molecular Dynamics simulations of quasi-2D water-glycerol liquid droplets, spreading on silica-like surfaces. The goal of the simulations is to quantify the contact line friction coefficient by regressing over the dynamic contact angle and the contact line speed. The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains configuration files and compressed output molecular trajectories. The contact angle and the contact line speed are computed from density maps binned on-the-fly using a customized Gromacs version (<a href="https://github.com/pjohansson/gromacs-flow-field">https://github.com/pjohansson/gromacs-flow-field</a>); frames are placed in a tarball ("spread-*p-r1.tar.gz"). The zipped folder 'scripts.zip' contains a self-contained library of functions to read density maps and a Jupyter notebook with an example of density reading and plotting. Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).

Spin Dynamics, Loop Formation and Cooperative Reversal in Artificial Quasicrystals with Tailored Exchange Coupling

The attachment contains the dataset for the manuscript entitled "Spin Dynamics, Loop Formation and Cooperative Reversal in Artificial Quasicrystals with Tailored Exchange Coupling".

Kinesin and Myosin Motors Compete to Drive Rich Multi-Phase Dynamics in Programmable Cytoskeletal Composites

Raw confocal microscopy data for 'Kinesin and Myosin Motors Compete to Drive Rich Multi-Phase Dynamics in Programmable Cytoskeletal Composites'

opencc-by-4.0Jul 2020View details →

The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments

This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at <a href="http://mapir.uma.es/work/uma-visual-inertial-dataset">http://mapir.uma.es/work/uma-visual-inertial-dataset</a>

Data Archive for James and Ross, 2023 (submitted): The Timing of the ENSO Spring Barrier in the Copernicus Dynamical Models

This archive contains data used in creating figures for James and Ross, 2023 (submitted): The Timing of the ENSO Spring Barrier in the Copernicus Dynamical Models. The data are provided in csv files, and the file "README" explains the contents of each file.

Database on agroforestry dynamics and adoption, Betampona, Madagascar 2018

SPSS Raw Database

Disentangling coastal groundwater level dynamics in a global dataset - data

Data to reproduce the research study "Disentangling coastal groundwater level dynamics in a global dataset" by Nolte et al. containing: <ol> <li>cluster_indices.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 45 index values</li> <li>cluster_attributes.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 28 attribute values</li> </ol> These data enable cluster analysis and random forest modeling. However, please note that the repository does not include raw groundwater level time series data. The code to reproduce the cluster analysis and random forest modeling as conducted in the study can be made available upon request from the first author.

Experimental files for Dynamics of plasmas produced by a laser pulse, inside a dense gaseous target, formed in an ambient gas

Time development of plasmas produced by interaction of laser pulses, with a nitrogen gas, was investigated. The interaction took place inside a small portion of dense nitrogen gas injected temporarily into a chamber filled with the gas under low pressure. High-temperature plasmas produced directly by the laser pulse were a source of soft x rays and charged particles, ionizing and exciting the surrounding gas. In this way, low-temperature plasmas were produced. The formation of high-temperature plasmas was studied using soft x-ray spectroscopy and x-ray streak imaging. Low-temperature plasmas formed at various distances from the laser focus were investigated using an optical streak camera. The experimental data.zip file contains the experimental data from an X-ray spectrograph, X-ray streak camera and optical streak camera. Work supported by the National Science Centre, Poland, Grant Agreement No. UMO-2020/39/B/ST2/00509

Deep and fast label-free Dynamic Organellar Mapping - Imaging data

Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c. Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X). Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei. Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.

EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems

Dataset of firmwares used for the experiments of the paper EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems

Dataset from "How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? Insights from observations and modeling in eastern Canada"

The dataset presented below is described in the publication “How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? An observational study in eastern Canada.” from Bouchard et al. (submitted) in the journal Hydrology and Earth System Science. The original dataset includes monitoring data collected at Montmorency Forest (47.29°N, 71.17°W) from 15 October 2020 to 15 June 2021 (W20-21) and from 15 October 2021 to 15 June 2021 (W21-22) in a medium-size gap, the small-size gap and under the canopy. The study site is a balsam fir – whit birch stand on a 12° slope of north-east aspect. In the monitoring dataset you can find at the hourly timestep: <ul> <li>Snow depth (cm)</li> <li>Soil temperature at 20 cm, 10 cm and 5 cm below ground surface (°C)</li> <li>Soil-snow interface temperature (°C)</li> <li>Snow temperature every 15 cm from the ground surface (°C)</li> <li>Snow surface temperature (°C)</li> <li>Air temperature (°C)</li> <li>Relative humidity (%)</li> <li>Soil volumetric water content at 15 cm below the ground surface (0 – 1)</li> </ul> The dataset also includes snow pit observations taken at Montmorency Forest during W20-21 and during W21-22. Each winter, four (4) snow pits were dug inside medium-size gaps, small-size gaps and at subcanopy locations. Snow pit measurement dates are presented in Bouchard et al. (submitted). Each snow pit includes the vertical profile of: <ul> <li>Snow stratigraphy</li> <li>Snow temperature</li> <li>Snow density</li> <li>Snow specific surface area (SSA)</li> </ul>   The snow pit height corresponds to the upper boundary of the topmost snow layer in the stratigraphy profile. For density measurements, the height value corresponds to the center of the 3-cm thick box cutter. For the SSA, the value is measured optically at the top of the sample. This value is representative of the top 1 cm of the snow sample, as this is the typical e-folding depth of 1310 nm radiation in snow. Grain type codes for the snowpack stratigraphy correspond to the International Classification for Seasonal Snow (Fierz et al., 2009): <ul> <li>PP: precipitation particles </li> <li>DF: decomposed and fragmented precipitation particles</li> <li>RG: rounded grains</li> <li>FC: faceted crystals</li> <li>FCxr: rounding faceted particles</li> <li>DH: depth hoar</li> <li>MFpc: melt forms – rounded polycrystals</li> <li>MF: melt forms – clustered rounded grains</li> <li>MFcr: melt forms – melt-freeze crusts</li> <li>IF: ice formations</li> </ul>

openJul 2023View details →

Dynamic corticothalamic modulation of the somatosensory thalamocortical circuit during wakefulness

Data and code to accompany "Dynamic corticothalamic modulation of the somatosensory thalamocortical circuit during wakefulness" by Dimwamwa, E. D., Pala, A., Chundru, V., Wright, N. C., Stanley, G. B., under review.

Primary Survey Socioeconomic Dataset of the Study on Agricultural Performance and Farm Size: Village Dynamics in South Asia (VDSA)

These datasets contain many economic variables related to agriculture like crop output value, profit and several others.   These datasets can be used for testing several hypotheses related to agricultural economics, both at plot level and household level. Users can also reproduce these datasets using the STATA 14 do file ‘VDSA data management for agricultural performance’. This STATA program file uses the Village Dynamics in South Asia (VDSA) raw data files in excel format. The resulting output will be two data files in stata format, one at plot level and other at household level.  These plot level and household level data sets are also included in this repository.   The word file ‘guidelines’ contain instructions to extract VDSA raw data from VDSA knowledge bank and use them as inputs to run the STATA do file ‘VDSA data management for agricultural performance’   The VDSA raw data files in excel format needed to run the stata do file are also available in this repository for users convenience The raw VDSA data were generated by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in partnership with Indian Council of Agricultural Research (ICAR) Institutes and the International Rice Research Institute (IRRI) and funded by the Bill & Melinda Gates Foundation (BMGF) (Grant ID: 51937).  The data were acquired in surveys by resident field investigators. Data collection was mostly through paper based questionnaires and Samsung tablets were also used since 2012. The survey instruments used for different modules are available at <a href="http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx">http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx</a> Study sites were selected using a stepwise purposive sampling covering agro-ecological diversity of the region. Three districts within each zone were selected based on soil, climate parameters as well as the share of agricultural land under ICRISAT mandate crops. On similar lines, one typical sub-district within each district and two villages within each sub-district were selected. Within each village, ten random households from four landholding groups were selected. Selected farmers were visited by well trained, agriculture graduate, resident field investigators, once every three weeks to collect information related to various socioeconomic indicators. Some of the data modules like details on crop cultivation activities including plot wise input, output was collected every three weeks while others like general endowments were collected once at the beginning of every agricultural year.  The compiled data, source data, data descriptions and data management code are all published in a public repository at <a href="http://dataverse.icrisat.org/dataverse/socialscience">http://dataverse.icrisat.org/dataverse/socialscience</a> at  <a href="https://doi.org/10.21421/D2/HDEUKU">https://doi.org/10.21421/D2/HDEUKU</a>] Some of the several benefits of these data are: <ul> <li>Scientists, students, development practitioners can benefit from these data to track changes in the livelihood options of the rural poor as this data provides long-term, multi-generational perspective on agricultural, social and economic change in rural livelihoods.</li> <li>The survey sites provide a socio-economic field laboratory for teaching and training students and researchers</li> <li>This dataset can be used for diverse agricultural, development and socio-economic analysis and to better understand the dynamics of Indian agriculture.</li> <li>The data helps to provide feedback for designing policy interventions, setting research priorities and refining technologies.</li> <li>Shed light on the pathways in which new technologies, policies, and programs impact poverty, village economies, and societies</li> </ul>

Dynamic spectra data for an interplanetary type III radio burst

Dynamic spectra data for an interplanetary type III radio burst from PSP, SOLO, STEREO and WIND.

Molecular dynamics dataset of apo TMPRSS2

This dataset contains all-atom molecular dynamics trajectories of TMPRSS2 (apo). The data was generated with openMM 7.4.0 with the CHARMM36 force field in the NPT ensemble at 310 K. Details about the molecular dynamics setup are given in Hempel, T.; Raich, L.; Olsson, S.; Azouz, N. P.; Klingler, A. M.; Hoffmann, M.; Pöhlmann, S.; Rothenberg, M. E.; Noé, F. Molecular Mechanism of Inhibiting the SARS-CoV-2 Cell Entry Facilitator TMPRSS2 with Camostat and Nafamostat. Chem. Sci. 2021, 10.1039.D0SC05064D. <a href="https://doi.org/10.1039/D0SC05064D">https://doi.org/10.1039/D0SC05064D</a>. The data was downsampled to a timestep of 1 ns and consists of three parts: <ul> <li> 1TMP_crystal7MEQpruned: Seeded from crystal structure PBD-ID 7MEQ, only catalytic chain. 80 µs cumulative simulation time.  </li> <li> 1TMP_crystal7MEQ: Seeded from crystal structure PBD-ID 7MEQ. Both chains. 77 µs cumulative simulation time.  </li> <li> 1TMP_homology3W94: Seeded from a homology model [S. Rensi et al., 2020, chemRxiv, DOI: 10.26434/ chemrxiv.12009582.]. 534 µs cumulative simulation time.  </li> </ul>