Find research datasets worth reusing

opencc-by-4.0Nov 2019View details →

Dataset: Occupancy Presence and Trajectory Dataset from an Instrumented Public Building

Dataset form three PC2, 3D stereo vision camera, form Xovis. It contains 5,485,350 readings, collected over 13 days, in the summer of 2019, in a public building in Denmark. The monitored space is 105 m2. The Data description can be found on: <a href="https://doi.org/10.1145/3359427.3361909">https://doi.org/10.1145/3359427.3361909</a> The occupancy_presence_and_trajectories.csv file contains the following attributes: <ul> <li>time:               Time-stamp when the entries were collected [Time].</li> <li>day_id:           ID for the specific day in the dataset [Number].</li> <li>workday:        Was the entry collected on a workday [Boolean].</li> <li>holiday:          Was the entry collected on a national holiday [Boolean].</li> <li>x:                    Represents the spatial position in X-axis [Number].</li> <li>y:                    Represents the spatial position in Y-axis [Number].</li> <li>occupant_id:  Unique Occupant-ID [Number]. </li> <li>height:            Occupant height in [millimeters]. </li> <li>camera_id:     Name of the camera which collected the entries [String].</li> </ul>

opencc-by-4.0Oct 2019View details →

Group diagrams for representing trajectories

The provided data is a  subset of the data set LifeTrack Geese IGRAS MPIO ICARUS maintained by movebank.org.  Please contact Andrea Kölzsch (akoelzsch@orn.mpg.de) before using the data in order to obtain essential information on how to correcty handle the data.  The provided code to compute group diagrams based on equal-time distance or Fréchet distance is at the stage of prototyping in order to support the findings of the corresponding paper "Group diagrams for representing trajectories" by Maike Buchin, Bernhard Kilgus, and Andrea Kölzsch. For questions about using the code, remarks and suggestions please contact Bernhard Kilgus (bernhard.kilgus@rub.de).

Amphetamine-like iron chelators trajectory files

Computional Data for  El Safadi, M., Wilson, K.A., Strudwicke, I., O'Mara, M., Bhadbhade, M., Rawling, T., and McDonagh A.M. (2024) Amphetamine-like Deferiprone and Clioquinol Derivatives as Iron Chelating Agents. Molecules, 29, 4213. (<a href="http://doi.org/10.3390/molecules29174213">doi.org/10.3390/molecules29174213</a>)

Chemical trajectory data file

Set of chemical trajectories for primordial gas generated with SGChem (CHEMISTRYNETWORK == 1), for use to train chemistry emulators. See CODES-Benchmark github repository for format details etc. Note: test data, use at your own risk.

Chemical trajectory data file - test version

Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study

In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (Δx=117 ± 4 μm, Δt=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks. Dataset A: Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted α-actinin, Connexin43 and DAPI immunostainings for Cellular Potts Model optimization. During data processing, cell shapes (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized. Dataset C corresponds to FluoVolt recordings (3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples).

Simulation trajectories for the article "Simulated Solute Tempering 2"

Simulation trajectories for the article "Simulated Solute Tempering 2". For each set of simulation is provided: <ul> <li>One .pdb file of the system without solvant and hydrogen atoms coordinates.</li> <li>Trajectorie files in .xtc format without solvant and hydrogen atoms coordinates for each of the 4 replicas.</li> <li>DataFrame files in .csv format containing: <ul> <li>Aim Temp (K)</li> <li>E solute scaled (kJ/mole)</li> <li>E solute not scaled (kJ/mole)</li> <li>E solvent (kJ/mole)</li> <li>E solvent-solute (kJ/mole)</li> <li>Step</li> <li>Potential Energy (kJ/mole)</li> <li>Total Energy (kJ/mole)</li> <li>Temperature (K)</li> <li>Speed (ns/day)</li> <li>Time(µs)</li> <li>lambda</li> </ul> </li> </ul> List of files: <pre><code>. ├── CLN025 │ ├── SST2 │ │ ├── CLN025_SST2_F_1.csv │ │ ├── CLN025_SST2_F_1.pdb │ │ ├── CLN025_SST2_F_1.xtc │ │ ├── CLN025_SST2_F_2.csv │ │ ├── CLN025_SST2_F_2.xtc │ │ ├── CLN025_SST2_F_3.csv │ │ ├── CLN025_SST2_F_3.xtc │ │ ├── CLN025_SST2_F_4.csv │ │ ├── CLN025_SST2_F_4.xtc │ │ ├── CLN025_SST2_F_6_rungs_1.csv │ │ ├── CLN025_SST2_F_6_rungs_1.xtc │ │ ├── CLN025_SST2_F_6_rungs_2.csv │ │ ├── CLN025_SST2_F_6_rungs_2.xtc │ │ ├── CLN025_SST2_F_6_rungs_3.csv │ │ ├── CLN025_SST2_F_6_rungs_3.xtc │ │ ├── CLN025_SST2_F_6_rungs_4.csv │ │ ├── CLN025_SST2_F_6_rungs_4.xtc │ │ ├── CLN025_SST2_F_6_rungs.pdb │ │ ├── CLN025_SST2_F_Tref_350_1.csv │ │ ├── CLN025_SST2_F_Tref_350_1.pdb │ │ ├── CLN025_SST2_F_Tref_350_1.xtc │ │ ├── CLN025_SST2_F_Tref_350_2.csv │ │ ├── CLN025_SST2_F_Tref_350_2.xtc │ │ ├── CLN025_SST2_F_Tref_350_3.csv │ │ ├── CLN025_SST2_F_Tref_350_3.xtc │ │ ├── CLN025_SST2_F_Tref_350_4.csv │ │ ├── CLN025_SST2_F_Tref_350_4.xtc │ │ ├── CLN025_SST2_U_1.csv │ │ ├── CLN025_SST2_U_1.pdb │ │ ├── CLN025_SST2_U_1.xtc │ │ ├── CLN025_SST2_U_2.csv │ │ ├── CLN025_SST2_U_2.xtc │ │ ├── CLN025_SST2_U_3.csv │ │ ├── CLN025_SST2_U_3.xtc │ │ ├── CLN025_SST2_U_4.csv │ │ └── CLN025_SST2_U_4.xtc │ └── ST │ ├── CLN025_ST_F_1.csv │ ├── CLN025_ST_F_1.xtc │ ├── CLN025_ST_F_2.csv │ ├── CLN025_ST_F_2.xtc │ ├── CLN025_ST_F_3.csv │ ├── CLN025_ST_F_3.xtc │ ├── CLN025_ST_F_4.csv │ ├── CLN025_ST_F_4.xtc │ ├── CLN025_ST_F.pdb │ ├── CLN025_ST_U_1.csv │ ├── CLN025_ST_U_1.xtc │ ├── CLN025_ST_U_2.csv │ ├── CLN025_ST_U_2.xtc │ ├── CLN025_ST_U_3.csv │ ├── CLN025_ST_U_3.xtc │ ├── CLN025_ST_U_4.csv │ ├── CLN025_ST_U_4.xtc │ └── CLN025_ST_U.pdb ├── PNGase_p97 │ ├── PNGase_p97_F_1.csv │ ├── PNGase_p97_F_1.xtc │ ├── PNGase_p97_F_2.csv │ ├── PNGase_p97_F_2.xtc │ ├── PNGase_p97_F_3.csv │ ├── PNGase_p97_F_3.xtc │ ├── PNGase_p97_F_4.csv │ ├── PNGase_p97_F_4.xtc │ ├── PNGase_p97_F.pdb │ ├── PNGase_p97_U_1.csv │ ├── PNGase_p97_U_1.xtc │ ├── PNGase_p97_U_2.csv │ ├── PNGase_p97_U_2.xtc │ ├── PNGase_p97_U_3.csv │ ├── PNGase_p97_U_3.xtc │ ├── PNGase_p97_U_4.csv │ ├── PNGase_p97_U_4.xtc │ └── PNGase_p97_U.pdb └── TrpCage ├── SST2 │ ├── TrpCage_SST2_F_1.csv │ ├── TrpCage_SST2_F_1.pdb │ ├── TrpCage_SST2_F_1.xtc │ ├── TrpCage_SST2_F_2.csv │ ├── TrpCage_SST2_F_2.xtc │ ├── TrpCage_SST2_F_3.csv │ ├── TrpCage_SST2_F_3.xtc │ ├── TrpCage_SST2_F_4.csv │ ├── TrpCage_SST2_F_4.xtc │ ├── TrpCage_SST2_F_Tref_350_1.csv │ ├── TrpCage_SST2_F_Tref_350_1.pdb │ ├── TrpCage_SST2_F_Tref_350_1.xtc │ ├── TrpCage_SST2_F_Tref_350_2.csv │ ├── TrpCage_SST2_F_Tref_350_2.xtc │ ├── TrpCage_SST2_F_Tref_350_3.csv │ ├── TrpCage_SST2_F_Tref_350_3.xtc │ ├── TrpCage_SST2_F_Tref_350_4.csv │ ├── TrpCage_SST2_F_Tref_350_4.xtc │ ├── TrpCage_SST2_U_1.csv │ ├── TrpCage_SST2_U_1.pdb │ ├── TrpCage_SST2_U_1.xtc │ ├── TrpCage_SST2_U_2.csv │ ├── TrpCage_SST2_U_2.xtc │ ├── TrpCage_SST2_U_3.csv │ ├── TrpCage_SST2_U_3.xtc │ ├── TrpCage_SST2_U_4.csv │ └── TrpCage_SST2_U_4.xtc └── ST ├── TrpCage_ST_F_1.csv ├── TrpCage_ST_F_1.xtc ├── TrpCage_ST_F_2.csv ├── TrpCage_ST_F_2.xtc ├── TrpCage_ST_F_3.csv ├── TrpCage_ST_F_3.xtc ├── TrpCage_ST_F_4.csv ├── TrpCage_ST_F_4.xtc ├── TrpCage_ST_F.pdb ├── TrpCage_ST_U_1.csv ├── TrpCage_ST_U_1.xtc ├── TrpCage_ST_U_2.csv ├── TrpCage_ST_U_2.xtc ├── TrpCage_ST_U_3.csv ├── TrpCage_ST_U_3.xtc ├── TrpCage_ST_U_4.csv ├── TrpCage_ST_U_4.xtc └── TrpCage_ST_U.pdb</code></pre>

Trajectories of Microsecond Simulations of the Orexin Receptor 2 System

This data set contains psf files and trajectories (.dcd files) of the performed simulations (16 systems in total) for the orexin receptor 2 system: 1) the orexin type 2 receptor (OX2R) in complex with lemborexant (nrk) or compound 1 (xgd), one replica per ligand 2) the orexin type 2 receptor (OX2R) in complex with orexin B (OxB), four replicas

Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere, version 2 (TOST-v2)

Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere, version 2 (TOST-v2) is improved and updated from 1970-2021 with seasonal, annual and decadal-monthly ozone climatology. TOST-v2 is provided on a grid of 5º × 5º × 1 km (latitude, longitude, and altitude) at 26 altitude levels in two altitude coordinates (by geometric levels and pressure levels), from two altitude starting levels (altitude above sea level and altitude above ground level), for three temporal resolutions (in the seasonal mean for each year, the annual means for each year from 1970 to 2021, and monthly means for each decade from the 1970s to the 2010s) and with three types of data fields (trop-stra, troposphere-only, and stratosphere-only) for users’ convenience. In addition, TOST-v2 also generated the corresponding datasets that show ozone variation at 3 percentile levels (25, 50 and 75th). Details of this dataset can be found in the data description file. If you use TOST dataset for related scientific research, please cite the below-listed corresponding reference (Liu et al., 2013a, b; Zang et al., 2024): <ul> <li>Liu, G., Liu, J., Tarasick, D., Fioletov, V., Jin, J., Moeini, O., Liu, X., Sioris, C., and Osman, M.: A global tropospheric ozone climatology from trajectory-mapped ozone soundings, Atmospheric Chemistry and Physics, 13, 10659-10675, 2013a.</li> <li>Liu, J., Tarasick, D., Fioletov, V., McLinden, C., Zhao, T., Gong, S., Sioris, C., Jin, J., Liu, G., and Moeini, O.: A global ozone climatology from ozone soundings via trajectory mapping: a stratospheric perspective, Atmospheric Chemistry and Physics, 13, 11441-11464, 2013b.</li> <li>Zang, Z., Liu, J., Tarasick, D., Moeini, O., Bian, J., Zhang, J., Thompson, A. M., Van Malderen, R., Smit, H. G. J., Stauffer, R. M., Johnson, B. J., and Kollonige, D. E.: An improved Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST): update, validation and applications, Atmospheric Chemistry and Physics, accepted, 2024.</li> </ul>

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation

ReaxFF simulations, QM calculation input/output files, and Jupyter notebooks for data analysis and visualization.

How to Drawjectory? - Trajectory Planning using Programming by Demonstration (Reproducibility Package)

The experiments were conducted following the scenario description from scenario_description.pdf. The scripts containing the DSL commands used for programming the trajectories can be found under /dsl_scripts. The evaluation was conducted using the R (v3.6.3 and tested on 4.4.1 too) programming language. The main script is experiments/evaluation.R which includes and sources all other necessary scripts. To re-run this script, make sure to open the R project (experiments/xperiment.Rproj) and have the following packages installed (note: other versions may also work):  <ul> <li>jsonlite (1.8.8)</li> <li>magrittr (2.0.3)</li> <li>dplyr (1.1.4)</li> <li>ggplot2 (3.5.1)</li> <li>wesanderson (0.3.7)</li> <li>pracma (2.4.4)</li> <li>SimilarityMeasures (1.4)</li> <li>progress (1.2.3)</li> </ul>

Trajectories of Microsecond Molecular Dynamics Simulations of the Orexin Receptor 2 System with Lemborexant

This data set contains psf files and trajectories (.dcd files) of the performed simulations for the orexin receptor 2 system: 1) the orexin type 2 receptor (OX2R) in complex with lemborexant, three replicas

Trajectories of Microsecond Molecular Dynamics Simulations of the Orexin Receptor 2 System with Compound 1

Molecular Dynamics Trajectories for GPR6 without Ligand (ApoNoLig)

Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3. This folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame. Periodic boundary condition (pbc) can be restored using VMD's standard pbc commands. Please cite us if you find this data useful!

Molecular Dynamics Trajectories for GPR6 with Ligand OLA

Molecular Dynamics Trajectories for GPR6 without Ligand (IagNoLig)

opencc-zeroFeb 2020View details →

dryad36/100

Experimental manipulation of perceived predation risk and cortisol generates contrasting trait trajectories in plastic crucian carp

Most animals constitute potential prey and must respond appropriately to predator-mediated stress in order to survive. Numerous prey also adaptively tailor their response to the prevailing level of risk and stress imposed by their natural enemies, i.e. they adopt an inducible defence strategy. Predator exposure may activate the stress axis, and drive the expression of anti-predator traits that facilitate survival in a high-risk environment (the predation–stress hypothesis). Here, we quantified two key morphological anti-predator traits, body morphology and coloration in crucian carp following exposure (or not) to a predator (pike) as well as to experimental manipulation of physiological stress via implants containing either cortisol or a cortisol inhibitor. We found that predator-exposed fish expressed a deeper-bodied phenotype and darker body coloration as compared with non-exposed individuals. Skin analyses revealed that an increase in the amount of melanophores caused the dramatic colour change in predatorexposed fish. Increased melanization is costly, and the darker body coloration may act as an inducible defence against predation, via a conspicuous signal of the morphological defence or by crypsis towards dark environments and a nocturnal lifestyle. By contrast, the phenotype of individuals carrying cortisol implants did not mirror the phenotype of predator-exposed fish but instead exhibited opposite trajectories of trait change: a shallow-bodied morphology with a lighter body coloration as compared with sham-treated fish. The cortisol inhibitor did not influence the phenotype of fish i.e. neither body depth nor body coloration differed between this group and predator-exposed fish having a sham implant. However, our results illuminate a potential link between stress physiology and morphological defence expression.

opencc-by-4.0Jun 2021View details →

Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19

Dataset of the publication: Codeço CT, Dal’Asta AP, Rorato AC, Lana RM, Neves TC, Andreazzi CS, Barbosa M, Escada MIS, Fernandes DA, Rodrigues DL, Reis IC, Silva-Nunes M, Gontijo AB, Coelho FC and Monteiro AMV (2021) Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19. Front. Public Health 9:647754. doi: 10.3389/fpubh.2021.647754

opencc-by-4.0Jun 2021View details →

DC-FSSH trajectory for 10D SBH model

It contains H5MD files for 2000 DC-FSSH trajectories for 10 Dimensional Spin-Boson Hamiltonian with energy based SDM decoherence correction scheme with timestep 0.5 fs. The simulations are performed using Newton-X NS.