Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,467
datasets available to search
ShareScore release 0.9.0
Dataset results
6,467 results for “process”
Figure 1b from: Keshavan A, Madan CR, Datta E, McDonough IM (2017) Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results. Research Ideas and Outcomes 3: e12276. https://doi.org/10.3897/rio.3.e12276
Figure 1b -
Figure 1a from: Keshavan A, Madan CR, Datta E, McDonough IM (2017) Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results. Research Ideas and Outcomes 3: e12276. https://doi.org/10.3897/rio.3.e12276
Figure 1a -
FORMATION OF A COMPLETE PERSON AND THE ROLE OF EDUCATION LESSONS IN THE PROCESS OF EDUCATION
Open the record for dataset details and reuse information.
Supporting data for: Effects of type and content of dissolved salts on primary characteristics of porous sandstones during maturing process
<p>This dataset includes experimental data used to generate the results detailed in the paper titled: Effects of type and content of dissolved salts on primary characteristics of porous sandstones during maturing process and submitted to Water Resources Research.</p>
[5G-IANA] UC1 - Data processed by the AI object detection algorithm
<p>Bounding boxes of objects detected by the artificial intelligence algorithm for each video frame received.</p>
Observation of the first stepping process of three winter lightning discharges
<p>Lightning location results data. This dataset includes complete data, which means it has not undergone quality control.</p>
Aqueous phase processing affects the formation and size distribution of aerosol organic functional groups during heavy pollution
<div> <p>Data from Hefei,a major urban city in Eastern China, including chemical composition, Infrared spectrum (IR), and pollution air data.</p> </div>
MGEP_HienNguyen_skill-rating questionnaires for advanced service design through Analytical Hierarchy Process_2022
<p>This presents a dataset of expert evaluation to determine what design skills for advanced services are important for the design team. The expert selection criteria were defined by the purposive sampling to recruit the appropriate experts through the chain sampling technique. Their evaluations were collected through the skill-rating questionnaires in accordance with Analytical Hierarchy Process (AHP). The resulting dataset was processed through the AHP algorithms programmed by R language. Transparency data and code availability may be (re)used by the design practitioners and/or researchers for replication and further analysis, offering the reproduceable research method by which multiple-criteria decision analysis is conducted.</p>
PKU GIMMS Processed NDVI data
<p>基于最大值合成法的PKU GIMMS NDVI年、月度数据处理</p>
CO2 reforming with methane for syngas production using a dielectric barrier discharge plasma coupled with Ni/γ-Al2O3 catalysts: Process optimization through response surface methodology
<p>XRD data, TG data, experimental data</p>
Processed data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Processed data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Fig. 6. Data processing from a in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 6. Data processing from a stack of 2D images to a 3D model. Image by MNHN.
MPM-CFD entrainment: Processed data and visualization
<p>Data supporting "The role of dilatancy and permeability of wet bed sediments eroded by a granular flow on erosion and runout: Two-phase MPM–CFD simulations". Content:</p> <p>- The <strong>full simulation results</strong> cannot be included as a supplementary material as the files are too large. However, we include the input files, which can be run with the MPM–CFD code (https://github.com/QuocAnh90/Uintah_NTNU/tree/1.0). We also include the processed data from the simulations, which can be used for visualization. </p> <p>- <strong>Processed data</strong> from the MPM–CFD simulations (particles positions and displacements, pore pressure).</p> <p> Each simulation is named with a code. For instance:</p> <ul> <li>dry / wes: dry or wet simulation</li> <li>30: indicates the effective friction angle</li> <li>k-3: -3 is the Log of the D_p parameter</li> <li>Name: data type of the particles: <ul> <li>particlesX: position</li> <li>particlesDispl: displacement</li> <li>particlespwp: pore pressure (absolute value, including "atmospheric pressure"), corrected with a default value of p_atm=101325 Pa</li> <li>particleStress: effective stress tensor</li> <li>pressCC: pore pressure (absolute value, including "atmospheric pressure"</li> </ul> </li> <li>m: material: 1=flow; 2=bed; 0=any material</li> <li>t= (time(s)+1) * 10</li> </ul> <p>- <strong>Matlab files</strong> to process the data and create the figures.</p> <ul> <li>The code Erosion_runout.m serves to plot flow and particle displacements and to calculate the flow runout and erosion volume</li> <li>The code pwp.m serves to plot pore pressure ratio and calculate the median pore pressure ratio</li> <li>The code pwp_evolution.m served to plot the time evolution of the median value of the pore pressure ratio</li> </ul> <p> </p> <p> </p>
The Unified Geometric Wave Theory: Updated Summary and mathematical model and process
<p>The Unified Geometric Wave Theory (UGWT) proposes that the universe's fundamental interactions are interconnected through waves, vibrations, and frequencies. This theory integrates concepts from quantum mechanics, general relativity, and sacred geometry, aiming to bridge the gap between quantum phenomena and macroscopic gravitational effects. The UGWT is currently in the empirical validation stage and seeks peer review for further refinement.</p>
USING EDUCATIONAL PROJECTS WITH THE HELP OF ICT IN THE EDUCATIONAL PROCESS
<p><em><span>This article reveals the problem of using the project method in the educational process with the help of information and communication technologies. The use of ICT tools when implementing a project in the educational process increases the effectiveness of students’ educational activities</span></em></p>
Example for USA metrological years 2020 and 2022 results data sets and post processing python code
<p>The files contain LCF model output data for 2020 and 2022 metrological years in the US as well as a python post-processing code to aggregate the reults of these files.</p>
FACTORS AFFECTING THE EMERGENCE OF INTEREST IN GAINING KNOWLEDGE AND METHODS OF ORGANIZING THE STUDY PROCESS
<p><em><span>maqolada innovatsion ta’lim davrida dolzarb bo‘lgan<span> </span>boshlang‘ich sinf o‘quvchilarida bilishga qiziqishini rivojlantirishning muhim omillari va usullari tahlil qilinadi va o‘quv jarayonini tashkil etish texnologiyasiga ta'sir qiluvchi omillarni o‘rganadi. Tadqiqot qiziqarli o‘quv materiallari, interaktiv o‘qitish usullari, o‘qituvchi o‘qitishga individual yondashuv, ijobiy fikr-mulohazalar va boshlang‘ich sinf o'quvchilarining bilishga qiziqishlarini rag‘batlantirish uchun sinfdan tashqari ishlarning ahamiyatini ko‘rib chiqadi. Ushbu omillarni tahlil qilish o‘quvchilarning bilishga qiziqishlarini rivojlantirish uchun o‘quv jarayonini samarali tashkil etish usullarini aniqlashga imkon beradi.</span></em></p>
Interview data for 'Risks in the offshore wind supply chain and tendering process impacts: Insights from industry expert elicitations'
<p>Updated 3 category labels to reduce potential for confusion. (v3)</p> <p>Interview data with restored functionality of some unused data analysis methods. (v2)</p> <p>Original upload. (v1)</p>
hela_mod3_processed
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.