Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

Dataset - Radially resolved dynamic inflow pitch step experiment in wind tunnel

The relevant turbine data of MoWiTO 1.8 and the preprocessed data presented in the following accepted paper are uploaded: Berger, F., Onnen, D., Schepers, J. G., and Kühn, M.: Experimental analysis of radially resolved dynamic inflow effects due to pitch steps, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-70, accepted, 2021.   The dataset describes a pitch step experiment with a model wind turbine in a wind tunnel between a high rotor load and a low rotor load. Measurements of turbine loads, near wake flow and radius resolved induction measurements are available.

opencc-zeroNov 2022View details →

Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit

Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined previous experience and current multisensory signals to estimate the hidden common source and subsequently updated the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how the premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.

opencc-by-4.0Jan 2023View details →

Raw dataset for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"

The dataset contains raw diffraction images in .tiff format. Each image file name contains three numbers, separated by "_". The first number refers to the order in which the images were taking in laboratory time. The second number refers to the absolute translation stage position in millimeters in the optical beam path of the pump beam. The stage position can be converted into a pump-probe delay time (taking into account the speed of light and a factor of 2 for the beam path, since the pulses move back and forth on the stage). Larger stage position values correspond to the optical pump pulse arriving later with respect to the probe pulse. Time zero was determined to be at 156.26 mm using a solid reference sample.

opencc-by-4.0Mar 2023View details →

Empirical Dataset of 1200 hours of Experiment: Reliability and Performance Measurements of Dynamic Routing Applications

This artifact presents the empirical measurements of an experiment with 1200 hours of runtime. We repeated the experiment 200 runs, each for 6 hours. The data folder provides the empirical data, and the plots folder gives the distribution of reliability and performance measurements in 200 runs.  For reliability, we simulate component crashes and measure the request loss. To simulate component crashes, we generate a random number for each service and router and stop the docker container if the generated number is below a crash probability (0.5% in this study).  For performance, we consider the round-trip time of requests going through several services until the requests are processed. The experiment cases are:  Four levels of call frequency \(cf\): 10, 25, 50, 100 requests/second (r/s) Three levels of service: 3, 5, 10 the number of services \(n_{serv}\) in a call sequence Three architectures:  Central Entity (CE): one router \(n_{rout} = 1\) Dynamic Routers (DR): three routers \(n_{rout} = 3\) Sidecar Architecture (SA): 1 router per service \(n_{rout} = n_{serv}\)

opencc-by-4.0Apr 2023View details →

Dataset for "Population-level transposable element expression dynamics influence trait evolution in a fungal crop pathogen"

Supplementary Tables Supplementary Table S1: SRA accession list of RNAseq reads. Supplementary Table S2: Genomic localization of TEs in gene elements and 10 kb windows upstream and downstream of the transcription start site (TSS) in the reference genome IPO323. Supplementary Table S3: Genome-wide TE insertion polymorphism (TIPs) in the pathogen population. 0 represents TE absence and 1 represents TE presence. Supplementary Table S4: Gene expression (log-transformed RPKM) values across the population. Supplementary Table S5: Locus-specific transcript abundance at individual TE loci (FPKM) across individuals. Supplementary Table S6: Percent of expressed copies within each TE family in the reference genome IPO323 and percent expressed TE copies in each TE family across the population. Supplementary Table S7: Linkage disequilibrium of TIP in the genome and neighboring SNPs within the 600bp distance from the TE loci. Supplementary Table S8: Genome-wide association mapping of the virulence-associated trait (PLACP: percent leaf area covered by pycnidia) and TE insertion polymorphisms in the genome. Supplementary Table S9: TIPs in the genome significantly associated with metabolite peak intensity variation in the pathogen population (filtered by Bonferroni threshold).

opencc-by-4.0May 2023View details →

Dataset for "Charge transfer dynamics in Ar+ + CO"

Dataset for "Charge transfer dynamics in Ar+ + CO"

opencc-by-4.0Jun 2023View details →

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.

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>

opencc-by-4.0Jul 2023View details →

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.

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>

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>

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>

Molecular dynamics dataset of barnase-barstar

This dataset contains all-atom molecular dynamics trajectories of barnase with its inhibitor barstar. All details regarding the molecular dynamics setup are given in reference [1]. The dataset consists of three parts and comes with a time step of 1 ns. amber-adaptive.tar: adaptive MD trajectories using Amber ff99SB. Cumulative length of about 0.3 ms. amber-gpugrid,.tar: longer simulations computed on GPUgrid, using Amber ff99SB. Cumulative length of about 1.7 ms. charmm-adaptive.tar: adaptive MD trajectories using CHARMM36. Cumulative length of about 0.5 ms. [1] Plattner, N.; Doerr, S.; Fabritiis, G. D.; Noé, F. Complete Protein–Protein Association Kinetics in Atomic Detail Revealed by Molecular Dynamics Simulations and Markov Modelling. Nature Chemistry 2017, 9 (10), 1005. <a href="https://doi.org/10.1038/nchem.2785">https://doi.org/10.1038/nchem.2785</a>.

Dataset for the paper: Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers

Dataset This dataset contains the input and output examples used in the publication described below. The hyperspectral folder contains the first example and use case. The spatiotemporal folder contains the second example use case. Contents data/ ├── hyperspectral │   ├── VeloxTest-Membranes.emd │   └── example_output │       ├── simulator-0-VeloxTest-Membranes.emd │       ├── simulator-0-VeloxTest-Membranes.json │       └── simulator-0-VeloxTest-Membranes.png └── spatiotemporal     ├── 2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.emd     ├── best-yolo.pt     └── example_output         ├── prediction-simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.mp4         ├── simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.emd         ├── simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.json         └── simulator-17-2023081040-700kx MultiFrams 991ms 600F Counting & FFI mode Falcon 700 kx 1819.mp4   Source Code Our code is hosted on GitHub at the following link: <a href="https://github.com/ramanathanlab/PicoProbeDataFlow/tree/main">https://github.com/ramanathanlab/PicoProbeDataFlow/tree/main</a> Paper Name Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers Paper Abstract The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day. While this data is highly important for both fundamental science and industrial applications, there is currently limited on-site infrastructure to handle these high-volume data streams. We address this problem by providing a software architecture capable of supporting large-scale data transfers to the neighboring supercomputers at the Argonne Leadership Computing Facility. To prepare for future scientific workflows, we implement two instructive use cases for hyperspectral and spatiotemporal datasets, which include: (i) off-site data transfer, (ii) machine learning/artificial intelligence and traditional data analysis approaches, and (iii) automatic metadata extraction and cataloging of experimental results. This infrastructure supports expected workloads and also provides domain scientists the ability to reinterrogate data from past experiments to yield additional scientific value and derive new insights.

opencc-by-4.0Sep 2023View details →

Molecular dynamics dataset of Trypsin-Benzamidine

This dataset contains all-atom molecular dynamics trajectories of Trypsin-Benzamidine. All details regarding the molecular dynamics setup are given in references [1, 2]. The dataset consists of two parts and comes with a time step of 100 ps. long-trajs.tar: cumulative of 100 µs of MD data in trajectories of 48 x 2 µs and 4 x 1 µs. Generated and used in [1]. gpugrid-trajs.tar: cumulative of 49.5 µs of MD data in trajectories of 495 x 100 ns. Generated for [2] and used in [1, 2]. [1] Plattner, N.; Noé, F. Protein Conformational Plasticity and Complex Ligand-Binding Kinetics Explored by Atomistic Simulations and Markov Models. Nature Communications 2015, 6, 7653. <a href="https://doi.org/10.1038/ncomms8653">https://doi.org/10.1038/ncomms8653</a>.  [2] Buch, I.; Giorgino, T.; De Fabritiis, G. Complete Reconstruction of an Enzyme-Inhibitor Binding Process by Molecular Dynamics Simulations. Proc. Natl. Acad. Sci. U.S.A. 2011, 108 (25), 10184–10189. <a href="https://doi.org/10.1073/pnas.1103547108">https://doi.org/10.1073/pnas.1103547108</a>.

publicMay 2022View details →

Dataset for analysing spatiotemporal dynamics of large carnivores in Kasungu National Park, Malawi

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publicApr 2021View details →

Eiders, nutrients and eagles: Bottom-up and top-down population dynamics in a marine bird_dataset

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