Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

Diffusion MRI quality control reports for the BTC datasets

Reports (HTML and pdf) of quality control on diffusion data on brain tumor patients before and after surgery, and controls, published at https://openneuro.org/datasets/ds001226 and https://openneuro.org/datasets/ds002080, based on Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019 Jan 1;184:801-812. doi: 10.1016/j.neuroimage.2018.09.073. Epub 2018 Sep 26. PMID: 30267859; PMCID: PMC6264528.

opencc-by-4.0Sep 2022View details →

simlation of Li diffusion via neutron scattering

don't even open

opencc-by-4.0Sep 2022View details →

KRAgamma cosmic-ray diffusion models

These files contain the "KRAgamma" models for the the diffuse gamma-ray maps due to to pi0, Inverse Compton and Bremsstrahlung emission associated to the Galactic cosmic-ray population. The Galactic cosmic-ray sea is propagated in the Galaxy with the DRAGON code, according to the transport setup described in     arXiv:1411.7623. The properties of the multi-TeV gamma-ray emission of the KRAgamma models are described in 1504.00227.

opencc-by-4.0Dec 2021View details →

High resolution solar irradiance variability climatology dataset part 1: direct, diffuse, and global irradiance

Dataset paper See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a>. Dataset description High resolution surface solar irradiance observations from the Baseline Surface Radiation Network (BSRN) of Cabauw, the Netherlands. This dataset spans 10 years, from 2011-02 until 2020-12-31. This dataset is the preprocessed 1 Hz observational record of direct, diffuse, and global horizontal irradiance, which is the basis for the official BSRN 1-minute dataset published at <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">PANGAEA</a>. Please refer to the official dataset for detailed metadata, instrument information, quality control flags, the full radiation balance and more (1 minute resolution). More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>, and more general information about BSRN is <a href="https://essd.copernicus.org/articles/10/1491/2018/">published on ESSD</a>. Part 1 of 2 This dataset is the basis for an analysis of surface solar irradiance variability, derived variables, supplementary meteorological data, and quicklooks, available in part 2 here: <a href="https://doi.org/10.5281/zenodo.7092058">https://doi.org/10.5281/zenodo.7092058</a> Usage disclaimer While resampling this 1-Hz data to 1-minute, with the official BSRN quality flags, should reproduce an identical dataset to the official 1-minute BSRN dataset, this has not yet been validated for this version. When you require the most reliable version of the radiation measurements, where variability at a higher resolution than 1 minute is not of concern, please refer to the official BSRN dataset at PANGAEA.

opencc-by-4.0Sep 2022View details →

Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''

This dataset contains the modified MAMEBUS source code, configuration files for the  MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.

opencc-by-4.0Feb 2022View details →

ORBi - Pourquoi diffuser en Open Access ?

Cette vidéo présente les avantages de publier en Open Access (notamment dans ORBi). Elle a été réalisée par le groupe de travail "Tutoriel" de ULiège Library et déposée sur Youtube le 21 février 2022 : <a href="https://youtu.be/4K424MwhIYE">https://youtu.be/4K424MwhIYE</a> Elle s’accompagne de deux fichiers adjuvants qui ont pour but d’expliquer et de partager les méthodes de travail du groupe dans sa création de ressources éducatives libres, à savoir :  <ul> <li> le script de la vidéo accompagné d’informations technico-pédagogiques en vis-à-vis (pour l’enregistrement studio de la ressource) </li> <li> la taxonomie des rôles et contributions utilisée pour créditer les différentes personnes ayant participé à la création de cette ressource (adapté de la taxonomie <a href="https://casrai.org/credit/">CRediT</a> (Contributor Roles Taxonomy) pour nos besoins.  </li> </ul> Un autre tutoriel ORBi est disponible ici : <a href="https://doi.org/10.5281/zenodo.7096800">ORBi - Que déposer dans ORBi (versions, type d'accès) ?</a>

Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion

This repository contains raw data and analysis routines of the publication “Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion” in Optics Express (doi.org/10.1364/OE.521702). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage. We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below. <div> <table> <tbody> <tr> <td> Name </td> <td> Description </td> <td> Parameters </td> </tr> <tr> <td> 10050, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> CS50-28, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> Mix, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated mixed sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> Dark, 10 us.mat </td> <td> Background interference intensity from a custom setup. </td> <td> Na=2048, Nb=5, 4.5 kHz </td> </tr> <tr> <td> Concentrated 8050, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Concentrated 9550, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Concentrated mix, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated mixed sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Dilute 8050, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Dilute 9550, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Dilute mix, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute mixed sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Chirp.data </td> <td> File containing k-interpolation data for the Thorlabs OCT measurements. </td> <td>   </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files. </td> <td>   </td> </tr> <tr> <td> Data_processing.py </td> <td> This module contains all analysis functions. </td> <td>   </td> </tr> <tr> <td> Custom_concentrated.py </td> <td> The script is for analyzing raw concentrated measurement files from the custom setup. </td> <td>   </td> </tr> <tr> <td> Thorlabs_concentrated.py </td> <td> The script is for analyzing raw concentrated measurement files from the Thorlabs setup. </td> <td>   </td> </tr> <tr> <td> Thorlabs_dilute.py </td> <td> The script is for running analysis of raw dilute measurement files from the Thorlabs setup. </td> <td>   </td> </tr> </tbody> </table> </div>

Data archive for: Chain forming diatoms use different strategies to avoid diffusion limited N assimilation

Data archive for: “Chain forming diatoms use different strategies to avoid diffusion limited N assimilation” published in Limnology and Oceanography (L&O), <a href="https://doi.org/10.1002/lno.12677">https://doi.org/10.1002/lno.12677</a>.   Dataset of single cell assimilation of DIC and NO3- by Skeletonema marinoi in the exponential and stationary growth phase captured using secondary ion mass spectrometry (SIMS) and stable isotopic tracers. The data set contains the data used in the study and the outliers excluded from further analysis.   Two strains (Strain 1 and Strain 2) were incubated during either the exponential or stationary growth phase over 24h with 15N enriched NO3- and 13C enriched DIC. The cell specific DIC and NO3- assimilation was measured using SIMS. See the main manuscript for a extensive experimental setup and more details. Each file is uploaded as both a .CSV and .XLSX, so that you can choose which you prefer.   Row description: Each row represents one Skeletonema marinoi cell. Column description: Single.cell: if the cell was a solitary cell (y) or not (n) End.cell: if the cell was located at the end of a chain (y) or not (n) Chain_position: a number assigned to identify every cell in a chain starting with 1 at one end of the chain Chain_length_numeric: the total number of cells in the chain Chain_length_max_6: the total number of cell in the chain, all numbers larger than 6 are pooled together and labelled >6 Two_chain: if the cell was in a chain consisting of only 2 cells (y) or not (n) Cell_information: whether the cell was a solitary cell (Single cell), found in a two cell chain (Two cell chain), found at the end of a chain longer than 2 cell (End cell), or in the middle of a chain longer than 3 cells (Middle cell). Chain: a identifying number assigned to differentiate the different chains C_fmol_per_cell_day: DIC assimilated in fmol cell-1 day-1 N_fmol_per_cell_day: NO3- assimilated in fmol cell-1 day-1

Data set related to the manuscript "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model"

Graphical files in the agr format for all the figures in the manuscript entitled "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model". XYZ files giving the particles and bulk positions in the lattices are also provided.

Data for "Probe molecular diffusivity in single ternary inorganic-organic microdroplets via interfacial ozonolysis of thiosulfate"

ExpXX_YTZS/G.xlsx: Integrated Raman intensity time profiles of thiosulfate for Expt. XX Raman_spectra_ExpXX.xlsx: Raman spectra before and after thiosulfate depletion via O3 for Expt. XX

Indirect social influence helps shaping the diffusion of innovations

This dataset accompanies the paper "Indirect social influence helps shaping the diffusion of innovations". It contains the participant choices between two colors when shown different information about the choices taken in their social network. The objective of the participants was to arrive to a consensus. One of the colors, which was the minority at the beggining, was given an extra incentive. Further details on the experimental setup are specified in the article. The main file is "Unified_data", which contains all the data from the 21 sessions studied and whose fields are described below. The file "ParticipantsData" contains the gender and age distribution of the participants, while the rest of the files are the individual data for each session, in the format given by oTree. Explanation of "Unified_data.csv" fields: <ul> <li>Experiment: Session number.</li> <li>participant.id_in_session: Identifier for each participant, constant for each session.</li> <li>config_name: Contains the order in which the different Setups were played. Its structure is of the form "egonetwork_1_a_b_c", where a,b,c are the numbers 2,3,4 in different order. These represent: 1-Setup AmAz (first neighbors), 2-Setup VerMag (first+second neighbors), 3-Setup RojNar (first+third neighbors), 4-Setup LilMor (first+fourth neighbors).</li> <li>Setup: Name of the Setup played (AmAz, VerMag, RojNar, LilMor and Instrucctions).</li> <li>node: Node assigned to the participant during that Setup.</li> <li>initialcolor: Initial color prescribed at the beginning of the Setup. Names are in spanish: Azul (blue) = #0072B2, Amarillo (yellow) = #FFC300, Verde (green) = #67E96E, Magenta = #C700AC, Rojo (red) = #E53950, Naranja (orange) = #FFA460, Lila (lilac) = #A792FC, Morado (purple) = #8000FF.</li> <li>InnovationAsInitialcolor: Binary variable showing if participant had the promoted color as initial color.</li> <li>threshold: Best threshold obtained in the model fitting.</li> <li>parameter: Best long-range parameter obtained in the model fitting.</li> <li>Time: Best time simulation obtained in the model fitting.</li> <li>AdoptersConsensus: First round in which all the participants chose the promoted color at least once.</li> <li>SimulationConsensus: Round in which the simulation of the fitted model arrived to consensus.</li> <li>Round: Round number.</li> <li>preg: Only in the Instructions Setup. Answer given to the control question.</li> <li>action: Color chosen by the participant in that round.</li> <li>Adopted_color: Previous varible translated into binary.</li> <li>Adopted_color_bin: Previous variable as logic variable.</li> <li>MajorityColor: Color most seen between the information given to the participant. In case of draw, one was chosen randomly.</li> <li>InnovationAsMajorityColorSeen: Binary variable showing if MajorityColor is equal to Promoted_color.</li> <li>bot: Binary variable showing if the participant was a bot in the current round (did not make a decision on time).</li> <li>color_neighbors_shown: List of the colors of the first neighbors shown to the participant in that round.</li> <li>color_neighbors_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_neighbors: Percentage of neighbors shown with color equal to the promoted color.</li> <li>color_friends_shown: List of the colors of the long-distance friends shown to the participant in that round.</li> <li>color_friends_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_friends: Percentage of long-distance friends shown with color equal to the promoted color.</li> <li>Adopted_innovation: Number of round since the participant first chose the promoted color.</li> <li>Promoted_color: Color with incentive. Setup AmAz - Azul, Setup VerMag - Magenta, Setup RojNar - Naranja, Setup LilMor - Morado.</li> <li>num_adopters: Number of adopters (people that have chosen the promoted color at least once) in the current round.</li> <li>total_participants: Constant showing the size of the network used (31).</li> <li>perc_adopters: Percentage of participants that are adopters in the current round.</li> </ul>

opencc-by-4.0Jun 2024View details →

Dataset for "Ion Diffusion Retarded by Diverging Chemical Susceptibility "

This dataset is for the paper "Ion Diffusion Retarded by Diverging Chemical Susceptibility" that will be published on Nature Communications.

dryad36/100

A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides

In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction-diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.

opencc-zeroJun 2024View details →

Movement Primitive Diffusion Demonstration Data

Training data for the experiments described in the paper "Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects". Movement Primitive Diffusion (MPD) is a diffusion-based imitation learning method for high-quality robotic motion generation that focuses on gentle manipulation of deformable objects. @article{Scheikl2024MPD, author={Scheikl, Paul Maria and Schreiber, Nicolas and Haas, Christoph and Freymuth, Niklas and Neumann, Gerhard and Lioutikov, Rudolf and Mathis-Ullrich, Franziska}, title={Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects}, journal={IEEE Robotics and Automation Letters}, year={2024}, volume={9}, number={6}, pages={5338-5345}, doi={10.1109/LRA.2024.3382529}, }

His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models

This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models" The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7. Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo. HMU-C dataset: part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a> part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a> HMU-1st dataset: part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a> part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a> part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a> part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a> Please fully download all the parts and concatenate them before extraction. Supplementary Data S1-S7: <a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a>

opencc-by-4.0Dec 2023View details →

Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: Breast tissue composition changes and collagen discriminative potential

This dataset serves as a comprehensive resource containing the raw data, analyses, and various tools utilized to produce the findings that are presented in our paper titled ‘Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: breast tissue composition changes and collagen discriminative potential’.  The aim of this clinical study is to test broad spectral range (635-1060 nm) time domain diffuse optical spectroscopy to monitor the response of breast cancer patients to neoadjuvant chemotherapy. Preliminary results from patients on changes in hemoglobin, water, lipids, collagen concentrations, and scattering parameters due to the therapy are presented. In these initial results, we hypothesize that collagen could be a possible biomarker to distinguish complete and partial responders.

The output data of the 1D Venusian chemistry-diffusion model of Dai et al. (2024) and the adopted chemical network

To use these data, please cite the paper: Dai et al. (2024, doi: 10.1051/0004-6361/202450552)  Supplemental_Tables_for_Dai_et_al_2024.pdf: the chemical network adopted in the model Nominal.txt: the chemical network adopted in the model modify_chem.py: the additional adjustments of the reactions Nominal_Bkzz_SO2.vul and A_Dkzz_SO2.vul: the output data of the nominal model and model A, respectively reading_data.py: the methods to read the output files   *Errata:  1) R296 in Supplemental Table: "1×10^7+0.05n_atm" should have been "1×10^17+0.05n_atm" 2) R329 in Supplemental Table: should have been removed The errata do not affect the results of this study.

Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models

The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data: Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures. Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths. The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design.

syedazerreza/Diffuse-Interferometer-Videos: Diffuse Interferometric Videos Version 1.0

Recorded videos for the diffuse interferometer