Find research datasets worth reusing

opencc-by-4.0Apr 2019View details →

Experimental data and benchmarks used in the paper "Nucleosome Dynamics: A new tool for the dynamic analysis of nucleosome positioning"

Experimental data used to illustrate the analysis with Nucleosome Dynamics pipeline. Three publicly available data sets were used: <ol> <li> Yeast metabolic cycle MNase-seq data downloaded from GEO under accession number GSE77631 corresponding to time points 9 and 12 Nocetti, N., and Whitehouse, I. (2016). Nucleosome repositioning underlies dynamic gene expression. Genes & Development 30, 660–672. </li> <li> MNase-seq data for S. cerevisiae cells synchronized in G1 and S phase, as described by Deniz (2016). Raw data available under accession number SAMEA2698380 Deniz, Ö., Flores, O., Aldea, M., Soler-López, M., and Orozco, M. (2016). Nucleosome architecture throughout the cell cycle. Scientific Reports 6, 19729. </li> <li> MNase-seq data for S. cerevisiae grown in different media: YPD, Gal, and EtOH. Data aligned to sacCer1 downloaded from GEO using accession numbers GSM351492, GSM351493, and GSM351494. Kaplan N, Moore IK, Fondufe-Mittendorf Y, Gossett AJ et al. The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 2009 Mar 19;458(7236):362-6. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/19092803">19092803</a></li> </ol> Each tar file contains two folders: <ul> <li>inputs: bam/RData files can be used to run Nucleosome Dynamics analyses. bigWig files contain nucleosome coverage and can be used to visualise in a genome browser. </li> <li>outputs: results from all analyses (nucleR, NFR, TSS, Periodicity, Stiffness, NucDyn)</li> </ul>   Simulated data used to benchmark nucleosome positioning by nucleR, and nucleosome dynamics by NucDyn, DANPOS and Dimnp. Figure 2B: synthetic data simulated for comparison of nucleR and Danpos to detect a second family of nucleosomes. Each folder pX contains simulations when the second nucleosome is present in X% of the families. Figure 2C: Distance between the dyads identified by nucleR and DANPOS to the dyad position in the true synthetic nucleosome map for fuzzy and well positioned nucleosomes. Figure 2D: Synthetic data used to compute sensitivity of the EVICTION prediction for NucDyn, DANPOS and Dimnp. Evictions were simulated removing reads from a given percentage of families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with point_log2FC < -1 and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software), and with default parameters for Dimnp  Figure 2E: Synthetic data used to compute sensitivity of the SHIFT prediction. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%).  For each simulated data: <ul> <li> *.RData files can be used to run nucleR or NucDyn (*mod* corresponds to the modified reads: eviction or shift introduced)</li> <li>*.bed files can be used to run DANPOS or Dimnp  (*mod* corresponds to the modified reads: eviction introduced)</li> <li>results/ folder contains results from DANPOS</li> <li>NR.gff contains the results from nucleR (*mod* corresponds to the results for modified reads: eviction or shift introduced)</li> <li>ND.gff contains the results from NucDyn</li> <li>res_dimnp_* contains the results from Dimnp</li> </ul> FigSupDanposShift: Synthetic data used to compute sensitivity of the SHIFT prediction for DANPOS. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with treat2control_dis-10 larger than the given displacement and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software). For each simulated data: <ul> <li> *.bed files contain the modified nucleosome positions</li> <li>results folder contains output from DANPOS</li> </ul>

opencc-by-4.0Jun 2019View details →

Experimental data and benchmarks of the paper "Merge-and-Shrink Task Reformulation for Classical Planning"

This dataset contains both benchmarks and data used in the paper. PDDL benchmark files can be found in the file optimal-strips-benchmarks.tar.gz and satisficing-strips-benchmarks.tar.gz, which contain the STRIPS domains from the sequential optimal/satisficing tracks of all IPCs as collected in the repository https://bitbucket.org/aibasel/downward-benchmarks. All other files in this dataset contain raw and processed data of all experiments, which were generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments can be found in the software bundle for this paper (see https://doi.org/10.5281/zenodo.3232878). Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.

opencc-by-4.0Jul 2019View details →

Experimental data from the PhD thesis "Certifying Planning Systems: Witnesses for Unsolvability"

The zipfile contains the raw experiment data used in the PhD thesis "Certifying Planning Systems: Witnesses for Unsolvability". It is split in three folders: 1) combined: contains the condensed data of all experiments as a json-file named properties, as well as a html page reporting the most important attributes 2+3) downward and fd-hc-refinement: contains the raw data of all experiments run with certifying-downward/certifying-fd-hc-refinement, as well as a json-file each for the condensed data in the *-eval directory. The json-file from 1) is a combination of those two json-files. Furthermore, it contains the downward-lab (https://doi.org/10.5281/zenodo.790461) scripts used for generating the experiments, showing with which configurations and limits the experiments were run.

opencc-by-4.0Sep 2019View details →

Data from: Experimental evidence that phosphorus fertilization and arbuscular mycorrhizal symbiosis can reduce the carbon cost of phosphorus uptake

Data from "Experimental evidence that phosphorus fertilization and arbuscular mycorrhizal symbiosis can reduce the carbon cost of phosphorus uptake". Functional Ecology

opencc-by-4.0Sep 2019View details →

Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data

All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13. Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=

opencc-zeroNov 2019View details →

Coherent soliton condensation in the optical event horizon (experimental data)

tar.gz-archive of experimental data for the article "Coherent soliton condensation in the optical event horizon" S. Bose (1,2), O. Melchert (1,3), I. Babushkin (1,3), M. Pal (2), U. Morgner (1,3), G. Steinmeyer (5,6), and A. Demircan (1,3) <ol> <li>Institute of Quantum Optics, Leibnitz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany</li> <li>Fiber Optics and Photonics Division, CSIR-Central Glass and Ceramic Research Institute (CGCRI), Kolkata, India</li> <li>Cluster of Excellence PhoenixD, Welfengarten 1, 30167, Hannover, Germany</li> <li>Max-Born-Institut, Max-Born-Straße 2A, 12489 Berlin, Germany</li> <li>Institut für Physik, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany</li> </ol>

Experimental raw data sets associated with certified reference material BAM-P115 (titanium dioxide) for comparison of nitrogen and argon sorption, available in the universal adsorption information format (AIF)

These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277. The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin. It is recommended to use the following requirements for the molecular cross-sectional area: Nitrogen: 0.1620 nm² Argon: 0.1420 nm² Expected specific surface area for nitrogen (BET): 140 to 154 m²/g Expected specific surface area for argon (BET): 129 to 135 m²/g Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material. The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon. These data are stored in the universal AIF format (adsorption information format), which allows flexible use of the data.

Experimental raw data sets associated with certified reference material BAM-P114 (titanium dioxide) for comparison of nitrogen and argon sorption, available in the universal adsorption information format (AIF)

These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277. The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin. It is recommended to use the following requirements for the molecular cross-sectional area: Nitrogen: 0.1620 nm² Argon: 0.1420 nm² Expected specific surface area for nitrogen (BET): 24 to 25 m²/g Expected specific surface area for argon (BET): 20 m²/g Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material. The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon. These data are stored in the universal AIF format (adsorption information format), which allows flexible use of the data.

opencc-by-4.0Jan 2024View details →

Data and Code for: An Experimental Test of Eco-evolutionary Dynamics on Rocky Shores

Abstract:  Despite a growing body of theoretical studies and laboratory experiments that have brought attention to the reciprocal impacts that ecological and evolutionary processes can have on one another, few studies have tested the importance of eco-evolutionary feedbacks in natural communities. We examined whether selection on natural phenotypic variation in a population of drilling dogwhelks (Nucella canaliculata) could impact rocky shore community dynamics. We performed a selection experiment raising newly-hatched dogwhelks on four diet treatments, reflecting natural variation in the abundance and shell thickness of prey species. Adult dogwhelks were tested in the laboratory on their ability to drill thick-shelled mussels. In addition, snails were outplanted to field cages to track the effects of dogwhelk phenotype on mussel bed succession. Despite our laboratory experiments suggesting that prey can impose selection and result in divergent consumer traits, successional patterns differed minimally based on the early-life diet of the dogwhelks.

Benchmark Experimental Data: Water-Wave Interactions with a Flexible Beam

<div> <h1>Experimental Data for the Experimental Modeling of Water-Wave Interactions with a Flexible Beam</h1> </div> This submission is based on the GitHub repository which was created to share the experimental data presented at the 42nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2023) in Melbourne, Australia in the form of a conference paper 'Experimental Modeling of Water-Wave Interactions with a Flexible Beam'[1]. The paper has already been published and is available but only behind a paywall. A talk has also been delivered at <a href="https://omae.secure-platform.com/a/solicitations/190/sessiongallery/schedule/items/13635" rel="nofollow">OMAE</a> 2023. The paper presents a series of fluid-structure-interaction (FSI) experiments for studying water-wave interactions with a flexible beam in a wide range of sea conditions thus yielding a variety of FSI test-case data. The experimental campaign is carried out at the Maritime Research Institute Netherlands's (MARIN's) concept basin. The concept basin is a 220m-long, 4m-wide and 3.6m-deep rectilinear basin with a carriage that can transverse along the basin's length.  The experimental setup includes a flexible beam which is fixed to the basin's carriage at one end while the other free end is submerged in the water. The setup is designed such that it admits the simultaneous measurements of incident waves and the beam's response. Hence, it is suitable for studying FSI problems. The details about the dimensions of the beam and arrangements of the sensors are described in the form of detailed CAD drawings which are given in CAD_fsi_beam_exp.pdf. The shared CAD drawings could be used in the future to reproduce the model.  The aim is to use these experimental data to validate FSI solvers commonly employed by the maritime industry in the design of fixed-foundation, offshore wind turbines. The study is divided into three experimental cases which are as follows (click on the case number to read more description): <ul> <li>Case-1 experiments: regular-water-waves interactions with the flexible beam when the carriage is at rest</li> <li>Case-2 experiments: regular-water-waves interactions with the flexible beam when the carriage is moving at a constant speed</li> <li>Case-3 experiments: irregular-water-waves interactions with the flexible beam when the carriage is at rest</li> </ul> <div> <div> <h2>FSI Experiments: Interactions of water-wave with a flexible beam</h2> </div> "All measurements are given in the form of .h5 format files, each of which has a corresponding .pan format file containing details of measurement names, units, frequency, maximum, minimum and standard deviation. The data presented is classified into different folders given as follows:" <ul> <li>Folder Exp1_carriage_rest_0.25m;</li> <li>Folder Exp1_carriage_rest_0.5m;</li> <li>Folder Exp2_carriage_moving_0.25m;</li> <li>Folder Exp2_carriage_moving_0.5m;</li> <li>Folder Exp3_irreg_waves_0.25m;</li> <li>Folder Exp3_irreg_waves_0.5m; and</li> <li>Folder hammer_test.</li> </ul> The description of the measurement and corresponding wave parameters are given in each folder. <div> <h2>Data organisation</h2> </div> All the main folders have several sub-folders and each sub-folder consists of mainly two types of files, i.e. .pan and .h5m. The files with extension .pan state the general information about experimental tests and sensors in text format. These .pan have three rows and the third row is divided into several columns. The second row states the information related to the experimental test, for example, the test number (80372_XXCB_XX_XXX_XXX_XX), project name (AEGRE), submerged depth of the beam (Proeven XX), gain, facility name (CB stands for concept basin), and scale (1.000). The first column of the third row shows the abbreviated sensor names which are explained in the table below. TABLE 1: The names and descriptions of the sensors are listed. <table> <tbody> <tr> <th>Name</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>C.SPEED</td> <td>Speed of the carriage</td> </tr> <tr> <td>WAVE.FORE</td> <td>Wave elevation measured by the probe located at the front of the beam (26.25 m away from the wavemaker)</td> </tr> <tr> <td>WAVE.SB</td> <td>Wave elevation measured by the probe located parallel to the beam (30 m away from the wavemaker)</td> </tr> <tr> <td>AX_i</td> <td>Accelerations of the beam in x-direction recorded by the accelerometer, where i denotes the accelerometer number</td> </tr> <tr> <td>AY_i</td> <td>Accelerations of the beam in y-direction recorded by accelerometer, where i denotes the accelerometer number</td> </tr> <tr> <td>AZ_i</td> <td>Accelerations of the beam in z-direction recorded by accelerometer, where i denotes the accelerometer number</td> </tr> <tr> <td>Flap 3 Pos</td> <td>Position of the waveflap wavemaker</td> </tr> </tbody> </table> The number with the accelerations, e.g. AX.1, AY.2, and AZ.3, denotes the position of the accelerometer along the beam. The response of the beam is dominant in the direction of wave, i.e. x-direction, therefore the .h5m files contain accelerations in the x-direction for all of the accelerometers. The accelerometers are numbered from 1 to 6, where accelerometer number 1 is at the submerged free end of the beam while accelerometer number 6 is located at the fixed end of the beam. The rest of the accelerometers are numbered 2 to 5 from the free end to the fixed end. The files with extension .h5m contain the actual time-domain measurements obtained from the sensors. Each .h5m from the experimental case contains acceleration signal from all six accelerometers in the x-direction, wave elevation measured by the probe that is 26.25 m away from the wavemaker, wave elevation measured by the probe that is 30 m away from the wavemaker, carriage speed, and variation waveflap position throughout the run. These measurements can be read with the help of post-processing code. The post-processing codes based on MATLAB and Python scripts, with comments, are shared. The names of the MATLAB and Python scripts are read_model_tst.m and read_model_tst.py respectively. Each script needs the name of the .h5m file as user input. In addition to reading the .h5m file, the script plots the signals from the sensors. For demonstration, the provided MATLAB script is used to plot the comparison of the wavemaker position with the wave elevation measured by the wave probe that is 26.25 m away from the wavemaker. <div> <h2>References</h2> </div> [1] Rehman, W., Bunnik, T., Bokhove, O. and Kelmanson, M. “Experimental Modeling of Water-Wave Interactions with a Flexible Beam.” Proc. ASME 2023 42nd Int. Conf. on Ocean, Offshore and Arctic Eng.: p. 10. 2023. ASME. </div>

Experimental online quantum dots charge autotuning using neural networks - Output data

Outputs of the model training and the online autotuning experiments presented in the paper: "Experimental online quantum dots charge autotuning using neural networks". Each folder in the zipped files represent a run that includes: <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> See README.txt for more information about the file strucutre.

opengpl-3.0-or-laterSep 2024View details →

Experimental data: Unmasking the Polygamous Nature of Quantum Nonlocality

The folders within the ZIP archive "tomography_data.zip" contain five- and six-qubit tomography data from permutational invariant tomography, see references in the paper. Each "stat" file contains two columns, the first is the efficiency-corrected number of counts for the particular projector, the second one the error of this number. The 2^n rows (for n qubits) are the different outcomes, starting with |0..0>, then |0..01>, ..., |1...1> for the particular measurement basis.  For any questions, please contact the authors of the corresponding paper. LK, 2024

opencc-by-4.0Sep 2024View details →

Experimental result data for primal-dual contextual Bayesian optimization

This dataset is the result of the paper "Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints" published in IEEE Conference on Decision and Control 2023.

opencc-by-4.0Sep 2024View details →

Data from: Experimental evolution of halophiles: rapid divergence along a multidimensional niche

This data was collected during the study entitled "Experimental evolution of halophiles: rapid divergence along a multidimensional niche." We explored multidimensional niche breadth evolution among two halophilic species, an archaeon (Halobacterium salinarum) and a bacterium (Salinibacter ruber). We propagated each species in rich and poor media for 60 generations and measured associated changes across novel conditions. In particular, we isolated the effects of selection history on axes of salinity and resource abundance, documenting whether shifts in niche breadth are context-dependent. Growth curves were generated via daily measurements over 5 days (4 replicate populations per treatment), with the area under the curve used as a proxy for absolute fitness in a given environment. Relative fitness was calculated as the fitness of the derived populations (following 10 transfers in their respective environment) divided by the fitness of the ancestral populations.

opencc-by-4.0Apr 2024View details →

Experimental data for the article "High Resolution Rovibrational Spectroscopy of the ν6 and ν3+v7 Bands of H2CCCH+"

data measured with COLTRAP apparatus 6th December - 11th December 2023  column 1 (x-axis) is frequency in cm-1, accuracy ~0.001 cm-1, precision ~0.0002 cm-1  column 2 (y-axis) are ion counts on mass 39u, integer  The data given here are original data with 14 concatenated data files.  Some spurious data points have been commented out by using "#"

opencc-by-4.0Sep 2024View details →

Experimental data used in the article entitled "Engineering an ultra-fine grained microstructure, twins and stacking faults in PBF-LB/M Al-Si alloy via KoBo extrusion method"

Dataset include EBSD results: KOB;O.ang and LPBF_condition.ang Tensile test results: KOBO-processed sample: KOBO.xls LPBF sample: SLM.xls

Accompanying data for the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification"

Experimental Data accompanying the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ijimpeng.2024.105083" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijimpeng.2024.105083</a> One (holed and double notched) specimen is dynamically loaded via an hydraulic tensile test machine (MTS-819, 20 kN) at 5 m/s. The specimen were cut from a 0.8 mm-thick DC04 (XES French standards) sheet in the rolling direction provided by ONERA. Reference image is captured using a high definition camera (29 Mpix, Prosilica GT from Stemmer) combined with the same objective lens than the one used for experiments. Deformed sample images are captured using the rotating mirror Ultra-high speed (HR-UHS) Cordin camera Model 580 at 68 kfps with a resolution of 3296 x 2472 pixels. The field of view is 35.8mm x 47.9mm leading to a pixel size of 14.49um. Ufreckles <a href="https://doi.org/10.5281/zenodo.1433776">10.5281/zenodo.1433775</a> is used to perform FE-based DIC using T3P1 linear triangular elements and a Tikhonov regularisation (over 3 elements). Eventually, kinematic data and load measurement  are used to identify stress fields via Data-Driven stress Identification (DDI) method. Are provided: <ul> <li>raw images, camera distortion modes and parameters, load net force and timeline</li> <li>kinematic fields obtained from Digital Image Correlation</li> <li>Stress fields identified using Data-Driven stress Identification</li> </ul> Matlab Codes to produce results (working with Ufreckles) <ul> <li>MultiSensor_DIC_script.m: in /Codes/ is the main script to run DIC</li> <li>Shape functions: in /Codes/shape_functions/ containing Zernike polynomial shape functions and deconvolution algorithm to get effective displacement from total displacement knowing camera distortions</li> </ul>

Experimental data generated on the thermal behaviour during the intrusion–extrusion of ZIF-8 with different salt solutions

/* ********** /* This work is licensed under a Creative Commons Attribution 4.0 International License. /* ********** Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union) along with the research on the thermal behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 3.1 (WP3). Underlying data for the publication Bartolomé, L. et al. Tuning Wetting-Dewetting Thermomechanical Energy for Hydrophobic Nanopores via Preferential Intrusion. The Journal of Physical Chemistry Letters 2023, 15, 880-887. https://doi.org/10.1021/acs.jpclett.3c03330. Data related to Figures 1, 3 and 4 in the article. Dataset Identifier: 10.5281/zenodo.13927359     Contact person:    Luis Bartolomé (CIC energiGUNE). ORCID: https://orcid.org/0000-0001-9649-1470 The archive 'JPCL_Tuning.zip' contains 2 folders with 64 files in total.

Geophysical Signals from Magma Propagation: Experimental and Processed Data

This repository contains the experimental data analyzed and interpreted in the manuscript titled "Geophysical signals induced by magma propagation: Insights from analog experiments" by S. Furst, J. Vandemeulebrouck, and V. Pinel. It includes video recordings, timelapse photos, accelerometer data, and deformation data. Additionally, there are three MATLAB scripts for post-processing the timelapse photos following the approach described in the manuscript. The results of the MFP analysis on the accelerometer data, as well as the outcomes from the COMSOL Multiphysics simulations, are also included in the repository.

Experimental and theoretical data generated on optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size

Open access to experimental data generated by the project <a href="https://www.electro-intrusion.eu/en">Electro-Intrusion</a> (101017858, Horizon 2020, European Union) along with the research on the mechanical behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). Underlying data for the publication Johnson, L. J. W. et al. Optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size: The role of hydrogen bonding between intruded and bulk liquid. Journal of Colloids and Interface Science 2023, 645, 775-783. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jcis.2023.04.059" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jcis.2023.04.059</a>. Data related to Figures 2, 4, 5 and S1 in the article.