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.
1,782
datasets available to search
ShareScore release 0.7.1
Dataset results
1,782 results for “algorithms”
Data from: Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm
Open the record for dataset details and reuse information.
Response of atmospheric river width and intensity to aquaplanet warming: A detection algorithm- and background moisture-independent approach
Open the record for dataset details and reuse information.
Data from: Development of a sustainability assessment algorithm and its validation using case studies on cryogenic machining
Open the record for dataset details and reuse information.
PIC: a data reduction algorithm for integral field spectrographs / data and reductions
<p>This zip file contains the data and reductions presented in the paper "PIC: a data reduction algorithm for integral field spectrographs"</p>
Data used in "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau"
<p>This is the data used in the manuscript "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau" (JGR earth surface 2020JF005564 ).</p>
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
<p>This repository contains input files from the synthetic, curated, and processed experimental single-cell gene expression datasets used in BEELINE.</p> <p>New in version 3:<br> 1) Ground-truth networks used for analysis of experimental scRNA-seq datasets for mouse and human datasets<br> 2) Changed license to CC BY-NC 4.0 from GPL v3.0 to account for the non-commercial clause for the network data</p>
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
Data from: QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants
Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.
PODC 2020 live recording, session: graph algorithms and congest model
<p>A recording of the "graph algorithms and congest model" session in PODC 2020. This session took place on Thursday, 6-Aug-2020, and was chaired by Alkida Balliu.</p>
PODC 2020 live recording, session: concurrency, self-* algorithms and more
<p>A recording of the "concurrency, self-* algorithms and more" session in PODC 2020. This session took place on Tuesday, 4-Aug-2020, and was chaired by Christoph Lenzen.</p>
PODC 2020 live recording, session: graph algorithms I
<p>A recording of the graph algorithms I session in PODC 2020. This session took place on Monday, 3-Aug-2020, and was chaired by Przemek Uznanski.</p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012) with uncertainty on the a priori plasma parameters obtained from the Plasma Line</p> <p>- Part VII) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm
<p>Data from the Paper: Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm.</p> <p>Abstract: </p> <p>Marine ecosystem models are important to identify the processes that affects for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximated an exemplary marine ecosystem model by different artificial neural networks. We used a fully connected network, then applied the sparse evolutionary training (SET) procedure, and finally applied a genetic algorithm (GA) to optimize both the network topology. With all three approaches, a direct approximation of the steady annual cycle was not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results were in very good agreement. In this way, we achieved a runtime reduction by about 15 \%. The result from the SET algorithm were comparable to those of the full network. Further application of the GA may lead to an even higher reduction.</p> <p>Content:</p> <p>Database sqlite <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN_Database.db">ANN_Database.db</a></p> <p>zip-files with data: </p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Data.zip">ANN-Data.zip</a> structure and weights of used networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Results.zip">ANN-Results.zip</a> results obtained with networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/Reference-Results.zip">Reference-Results.zip</a> reference results and training data</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for: Sensitivity of a satellite algorithm for harmful algal blooms discrimination to the use of laboratory bio-optical data for training
<p>Two files relating to the publication by Martinez-Vicente et al. (2020).</p> <p>meris_data_karenia_alt_chla.xlsx : file containing the chlorophyll concentrations for the different areas in the MODIS images selected for training and evaluation of the algorithm.</p> <p>coefficients_for_LDA_Karenia_mikimotoi.zip: file containing the coefficients for the Linear Discriminant Analysis (LDA) resulting from the training datasets 1,2 and 3. </p> <p> </p>
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
<p>This project contains datasets used for Temporal Knowledge Base Completion (TKBC) paper [1].<br> Find more details here: https://github.com/dair-iitd/tkbi</p> <p>[1] "<a href="https://arxiv.org/abs/2005.05035">Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols</a>" Jain, Prachi*, Sushant Rathi*, Mausam and Soumen Chakrabarti. EMNLP 2020.</p>
Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network (video)
Full video presentation of the paper: Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Secured Distributed Algorithms without Hardness Assumptions (video)
Full video presentation of the paper: Secured Distributed Algorithms without Hardness Assumptions.<br><br>Appears in Session 4 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Time-Plan Optimization with Genetic Algorithm for Regain of Energy from Train Tracks
<p>Dataset using for Time-Plan Optimization with Genetic Algorithm for Regain of Energy from Train Tracks</p>
Data from: Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black-browed albatrosses
Species distribution models (SDM) based on tracking data from different devices are used increasingly to explain and predict seabird distributions. However, different tracking methods provide different data resolutions, ranging from < 10m to >100km. To better understand the implications of this variation, we modeled the potential distribution of black-browed albatrosses Thalassarche melanophris from South Georgia that were simultaneously equipped with a Platform Terminal Transmitter (PTT) (high resolution) and a Global Location Sensor (GLS) logger (coarse resolution), and measured the overlap of the respective potential distribution for a total of nine different SDM algorithms. We found slightly better model fits for the PTT than for GLS data (AUC values 0.958±0.048 vs. 0.95±0.05) across all algorithms. The overlaps of the predicted distributions were higher between device types for the same algorithm, than among algorithms for either device type. Uncertainty arising from coarse-resolution location data is therefore lower than that associated with the modeling technique. Consequently, the choice of an appropriate algorithm appears to be more important than device type when applying SDMs to seabird tracking data. Despite their low accuracy, GLS data appear to be effective for analyzing the habitat preferences and distribution patterns of pelagic species.
Data from: Can the use of digital algorithms improve quality care? An example from Afghanistan
Background Quality of care is a difficult parameter to measure. With the introduction of digital algorithms based on the Integrated Management of Childhood Illness (IMCI), we are interested to understand if the adherence to the guidelines improved for a better quality of care for children under 5 years old. Methods More than one year after the introduction of digital algorithms, we carried out two cross sectional studies to assess the improvements in comparison with the situation prior to the implementation of the project, in two Basic Health Centres in Kabul province. One survey was carried out inside the consultation room and was based on the direct observation of 181 consultations of children aged 2 months to 5 years old, using a checklist completed by a senior physicians. The second survey queried 181 caretakers of children outside the health facility for their opinion about the consultation carried out through the tablet and prescriptions and medications given. Results We measured the quality of care as adherence to the IMCI's guidelines. The study evaluated the quality of the physical examination and the therapies prescribed with a special attention to antibiotic prescription. We noticed a dramatic improvement (p<0.05) of several indicators following the introduction of digital algorithms. The baseline physical examination was appropriate only for 23.8% [IC% 19.9-28.1] of the patients, 34.5% [IC% 30.0-39.2] received a correct treatment and 86.1% [IC% 82.4-89.2] received at least one antibiotic. With the introduction of digital algorithms, these indicators statistically improved respectively to 84.0% [IC% 77.9-88.6], >85% and less than 30%. Conclusions Our findings suggest that digital algorithms improve quality of care by applying the guidelines more effectively. Our experience should encourage to test this tool in different settings and to scale up its use at province/state level.
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.