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1,782 results for “algorithms”

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zenodo32/100

Training data for ship track detection machine learning algorithms

<p>The training data and labels used to train the linked machine learning algorithm</p>

opencc-by-4.0Aug 2022View details →
zenodo32/100

Effectiveness of the spectral area index created by three algorithms for tree species recognition

<p>This dataset is an experimental dataset used to extract PAIs and verify their effectiveness in tree species classification.</p>

opencc-by-4.0Sep 2022View details →
zenodo32/100

Automated Verification for Real-Time Systems via Implicit Clocks and an Extended Antimirov Algorithm

<p>This is the artifact for paper</p> <p>&quot;Automated Verification for Real-Time Systems via Implicit Clocks and an Extended Antimirov Algorithm&quot;</p> <p>The latest code can be found in&nbsp;https://github.com/songyahui/Timed_Verification</p> <p>Our benchmark contains three folders:</p> <p>- Validation: We manually annotate TimEffs specifications for a set of synthetic examples (for about 54 programs), to test the main contributions, including: computing effects from symbolic timed programs written in C^t ; and the inclusion checking for TimEffs with the parallel composition, block&nbsp;waiting operator and shared global variables.</p> <p>- Experiment1: For 16 C^t programs, and the annotated temporal specifications are in a 1:1 ratio for succeeded/failed cases. We record the evaluation results in Table 3 in the paper.</p> <p>- Experiment12: comparison with the PAT [16] model checker using real-life Fischer&rsquo;s mutual exclusion algorithm.</p>

opencc-by-4.0Oct 2022View details →
zenodo32/100

Environmental Epigenetics- Proposed Algorithm for Pediatric Cancer Susceptibility

Environmental Epigenetics- Proposed Algorithm for Pediatric Cancer Susceptibility

opencc-by-sa-4.0Mar 2017View details →
zenodo32/100

Benchmarks and Results of Experiments Evaluating Various Proof Compression Algorithms

<p>These experiments were discussed in a paper published at the Tableaux 2013 conference.</p>

opencc-by-nc-sa-4.0Dec 2012View details →
zenodo32/100

Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'

<p>1. &quot;Retrieval_Database&quot; file contains the pre-calculated retrieval database.</p> <p>2. &quot;Algorithm_Input&quot; file&nbsp;contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. &quot;TrueProfiles&quot; file contains the true profiles corresponding to the input brightness temperatures.&nbsp;</p> <p>4. &quot;Algorithm_Output&quot; file contains the output of the ensemble optimization retrieval algorithm.</p>

opencc-by-4.0Jan 2018View details →
zenodo32/100

Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems (Code and Dataset)

<p>This is the code and the dataset for our paper published at IEEE WCCI/CEC 2024, <a href="https://2024.ieeewcci.org/">https://2024.ieeewcci.org</a>.</p> <p>Title: "Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems"<br>Abstract: Parameter control refers to the techniques that dynamically adapt the parameter values of the evolutionary algorithm during the optimization process, such as population size, crossover rate, or operator selection. Adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. With this article, we contribute a systematic investigation of an adaptive, multi-objective algorithm that is designed for the optimisation of integer decision spaces. We find that (1) adaptation outperforms the best static configurations, and (2) performance of the multi-objective algorithm is often independent of the adaptation scheme's initial configuration.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Data, scripts, and plots for the paper ICCP "Learning to Select Scheduling Algorithm in OpenMP"

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →
zenodo32/100

ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. Results of tropical cyclone tracking algorithm for TC Irma, Florence, Teddy and Ida simulations with ECMWF IFS.

<p>ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. The data is from forecasts of tropical cyclone Irma, Florence, Teddy and Ida performed at horizontal atmosphere resolutions of TCo1279, TCo2559, TCo3999, TCo7999 and ocean resolutions of eORCA025 and eORCA025.</p> <p>&nbsp;</p> <p>Data also contains the results of tracking these tropical cyclones in the ECMWF IFS simulations.&nbsp;</p>

opencc-by-4.0May 2024View details →
zenodo32/100

KACO Algorithm Experimental Raw Data

<p>This archive contains the raw experimental results for the KACO algorithm test. The data is divided by domain, and by algorithm. Each subfolder will contain the results of the algorithm, the computed pheromone density data, and the convergence data as well.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Appendix to "Process Mining Pipelines with Controlled Sharing of Data and Algorithms"

<p><strong>Abstract: </strong>Process mining leverages execution traces within an organisation's IT systems to gain insights into its processes. Despite being a mature discipline in academia and industry, setting up process mining pipelines is still a complex task and involves programming, manual steps, and considerations of privacy and intellectual property.</p> <p>This paper introduces a platform based on a distributed architecture that helps define, deploy, and execute process mining pipelines across organisations. The requirements for this distributed architecture and platform are derived from a set of process mining scenarios, whose relevance is validated through a survey.</p> <p>Furthermore, this paper introduces a prototype for an initial version of the platform, demonstrating feasibility and supporting the specified requirements. This development is a major step in advancing process mining, offering simpler and more efficient ways of implementing and managing complex process mining pipelines on a larger scale.</p> <p><strong>Description: </strong>This dataset presents the support for non-functional requirements identified in the paper "Process Mining Pipelines with Controlled Sharing of Data and Algorithms" by existing process mining platforms.</p> <p><strong>Legend:</strong> Green cells indicate complete fulfilment. Yellow indicates partial fulfilment. Blue cells indicate uncertain fulfilment. Red indicates no fulfilmnet.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Methodology based on the MaxEnt algorithm for modelling habitat distribution

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opencc-by-4.0Jun 2024View details →
zenodo32/100

Determination of Optimal Thermal Comfort Conditions in the Iron Smelting Industry Work Environment Using Firefly Algorithm

<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Implementation of K-Nearest Neighbor Algorithm and Gray Level Co-Occurance Matrix Method in Mushroom Type Classification

<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Optimized Ensemble Algorithm of SVMD-SVR+BiLSTM for Short-Term Forecasting of Earth Rotation Parameters

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opencc-by-4.0Jul 2024View details →
zenodo32/100

Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin

<p>This archive contains masked data over the Susquehanna River Basin and associated Python code to reproduce the findings of the manuscript "Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin." Additional water year analysis not included in the manuscript are also tarred in this archive.</p>

openagpl-3.0-or-laterDec 2023View details →
zenodo32/100

Experimenting with Adaptive Bitrate Algorithms for Virtual Reality Streaming over Wi-Fi

<p>Dataset of resulting files from capturing VR traffic in Wi-Fi 6 of a fork of the Air Light Virtual Reality (ALVR) software, used to stream games from a PC to a VR HMD in real time. The dataset includes:&nbsp;</p> <ul> <li>Parsed Wireshark captures in TSV format, both captured from server and network emulator, and corresponding ALVR session log are found for each experiment. In each folder, all files of netem, server or ALVR are found (with names corresponding to the emulated network effect, which is applied via the netem computer). We are using Constant BitRate (CBR) for each test, at 100 Mbps. The plots are added in the corresponding folder for each effect, and a metric comparison between WS and ALVR.&nbsp;</li> <li>ALVR session logs for a comparison on the logged metrics under tests of Mobility, using different strategies for bitrate adaptation: CBR, ABR and our own contribution.</li> <li>ALVR session logs for a comparison on the logged metrics under tests of emulated capacity drops, using different strategies for bitrate adaptation: CBR, ABR and our own contribution.</li> </ul> <p>The Wireshark captures have been parsed from a PCAPNG into a CSV file (via tshark) containing the principal fields of each packet separated by a space (TSV format). Since the PCAPNG captures were over 1 GB each, we keep only a subset of the first bytes of the payload and the main fields, and discard the rest. There are additional CSV files for TCP UL packets, which we parsed separately from the same captures for us to validate the measured RTT of ALVR.</p> <p>The ALVR session logs contain raw json strings in .txt format, logged from the server using our fork of ALVR. We're using some additional events from the ones ALVR originally used, in order to log our metrics at arbitrary points in the code.&nbsp;</p> <p>The first 22 bytes of the payload in each packet are used to parse into the StreamSocket fields that ALVR uses, and record timestamps to validate the metrics of ALVR manually; which can be used to reproduce our results. Namely, each row of the csv (frame.time_relative, ip.src, ip.dst, frame.len, data.data) contains the timestamp of the packet, its IP source, destination, length and first 22 bytes of the payload as a hexadecimal string.</p> <p>To analyze the CSV via python, the original StreamSocket fields can be recovered for each packet, using iteratively the following lines:&nbsp;</p> <div> <div> <blockquote> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; data_bytes = bytes.fromhex(row["data.data"])</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; shard_length, stream_id, packet_index, shards_count, shard_index, _ &nbsp;= struct.unpack("&gt;IHIIII", data_bytes)</div> </blockquote> </div> </div> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Implementation of a Diabetes Status Prediction Application Using a Machine Learning Algorithm Approach

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →
zenodo32/100

Raw Data Archive for "A New Cross-Calibration Algorithm for Multi-Mode Secondary Electron Multiplier Detectors"

<p>Raw Thermo Element XR data files for use with the Enhanced Linear Dynamic Range (ELDR) Model.&nbsp; The model inverts raw pulse and analog data from the Element 2/XR inductively coupled plasma mass spectrometer to estimate detector cross-calibration, linear-in-time cross-calibration drift, and detector non-linearity.&nbsp; This data archive contains the raw data comprising 1572 individual laser ablation analyses.&nbsp; Each analysis consists of two files: (1) raw data archive (.dat); and (2) setup information file <em>(</em>.inf).&nbsp; &nbsp;The analytical session is divided into three sequences: SEQ1, SEQ2, SEQ3.</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Verification Algorithms for Automated Separation Logic Verifiers (Artifact)

<p>This is the artifact for the CAV 2024 paper "Verification Algorithms for Automated Separation Logic Verifiers". For more information, see the description of the artifact in "artifact_overview.pdf". The data in the artifact (minus the implementations of the benchmarked algorithms) can also be found on GitHub: https://github.com/marcoeilers/CAV24-data.</p>

opencc-by-4.0Jul 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record