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.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “Spark”
Characterizing Distributed Machine Learning Workloads on Apache Spark
<p>This dataset was used for our submission at Middleware'22 titled: "Characterizing Distributed ML Workloads"</p> <p>It will contains the description and the raw data, its format, as well as a detailed description of the cluster deployments used by these experiments.<br> </p> <p>The full paper is available here:</p> <p>https://dl.acm.org/doi/10.1145/3590140.3629112</p>
SNSF Spark project "Sonic Imagination" – Video documentation of on-site scenarios and interview excerpts
<p>We aimed to investigate the hypothesis that virtual sound sources in audio augmented environments are particularly suitable for triggering and directing imaginations within human inner perception. Our hypothesis was that the binaural listening to imaginary entities at the place of recording (so to speak 'in-situ') enhances the imagination in a special way, since sound as a non-visual medium favours the creation of images in human inner perception. We narrowed down our research question especially in regards to our practical experimentations to possible applications in areas that rely on the guided creation of individual or intersubjective imaginations, such as urban planning and scenario development, certain forms of trauma therapy, as well as audiovisual art and fiction.</p> <p>Further information: </p> <p><a href="https://www.ludwigzeller.net/projects/sonic-imagination/">https://www.ludwigzeller.net/projects/sonic-imagination/</a></p> <p><a href="https://p3.snf.ch/project-195868">https://p3.snf.ch/project-195868</a></p> <p> </p>
Lichen speciation is sparked by a substrate requirement shift and reproduction mode differentiation
<p>We show that obligate lignicoles in lichenized Micarea are predominately asexual whereas most facultative lignicoles reproduce sexually.AQ1 Our phylogenetic analyses (ITS, mtSSU, Mcm7) together with ancestral state reconstruction show that the shift in reproduction mode has evolved independently several times within the group and that facultative and obligate lignicoles are sister species. The analyses support the assumption that the ancestor of these species was a facultative lignicole. We hypothezise that a shift in substrate requirement from bark to wood leads to differentiation in reproduction mode and becomes a driver of speciation. This is the first example of lichenized fungi where reproduction mode is connected to substrate requirement. This is also the first example where such an association is demonstrated to spark lichen speciation. Our main hypothesis is that obligate species on dead wood need to colonize new suitable substrata relatively fast and asexual reproduction is more effective a strategy for successful colonization.</p>
Dataset for Stack overflow Manual Results about challenges in developing Spark applications
<p>This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications".</p> <ul> <li>the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. </li> </ul> <p> </p> <ul> <li>the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <ul> <li><em>query_data.sql</em> is used to collect data from the Stack Exchange website.</li> <li><em>sample.py</em> is used to sample data for the manual analysis in the paper.</li> <li><em>common_issue.py</em> is used to study the percentage of common issues in rq1. </li> <li><em>popularity.py </em>is used to calculate the average of normalized view counts in rq2.</li> <li><em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2.</li> <li><em>root_cuase.py</em> is used to study the percentage of root causes in rq3. </li> </ul> </li> </ul>
Self-organized transport model of spark discharge development and its application to the process of lightning initiation in a thundercloud
<p>This video visualizes results obtained by a small-scale transport model of electrical discharge formation in a thundercloud. The model discharge tree is a dynamic graph, nodes and edges of which are capacitive and conductive elements, respectively, electrical parameters of which vary with time. In the framework of the used approach, a heated well-conducting lightning leader channel is formed by combining the currents of tens of thousands of streamers, each of which initially has a negligible conductivity and a temperature, which does not differ from the ambient value. The model leader has electrical characteristics, which are intermediate between the laboratory long spark and the developed lightning channel, which is expected for an “immature” lightning leader.</p>
Spark Data containing logs and metrics (KPIs) for Hades
<p>Please make sure to cite our paper whenever you use the data in your research:</p><p>@inproceedings{DBLP:conf/icse/LeeYCSYL23, author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Yongqiang Yang and Michael R. Lyu}, title = {Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention}, booktitle = {45th {IEEE/ACM} International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, May 14-20, 2023}, pages = {1724--1736}, publisher = {{IEEE}}, year = {2023}, url = {https://doi.org/10.1109/ICSE48619.2023.00148}, doi = {10.1109/ICSE48619.2023.00148}, timestamp = {Wed, 19 Jul 2023 10:09:12 +0200}, biburl = {https://dblp.org/rec/conf/icse/LeeYCSYL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }</p><p> </p><p> </p>
On the Space Leader Evolution During the Breakthrough Phase of Attachment Process in Positive Laboratory Sparks
<p>The data supports the manuscript entitled "On the Space Leader Evolution During the Breakthrough Phase of Attachment Process in Positive Laboratory Sparks." The "Data 1" group contains the discharge current waveform, the normalized gray value, and the synchronized voltage-current-impedance waveform. The files in "Data 1" group can be opened by Excel. The "Data 2" group includes high-speed video frames for five discharge events and they can be opened by Phantom Camera Control (PCC) software. The data can be used freely for scientific purposes with appropriate citation.</p>
Real-time Welding Sparks Detection on Construction Sites us-ing Contour Detection and Deep Learning
<p>One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regards to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue-saturation-value. Additional filtering methods regarding non-welding zone and contour area-based filter, were also newly developed to enhance prediction accuracy of welding sparks. The method was evaluated using 230 welding sparks images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding sparks detection for enhancing fire surveillance at construction sites.</p>
Long-Term Safety and Efficacy of Spark-Sponsored Gene Therapies in Males With Hemophilia A
ClinicalTrials.gov study NCT03432520. IPD Sharing: Not stated. Countries: 3. Publications: 1.
SPARK-ALL: Calaspargase Pegol in Adults With ALL
ClinicalTrials.gov study NCT04817761. IPD Sharing: YES. Countries: 1. Publications: 1.
Effectiveness of the Supportive and Palliative Care Review Kit (SPARK) for Cancer Patients in the Acute Hospital
ClinicalTrials.gov study NCT03330509. IPD Sharing: Not stated. Countries: 1. Publications: 3.
NST-SPARK: Preliminary Study of an Augmented Reality (AR) App Delivering Recovery-Oriented Cognitive Therapy for Negative Symptoms in Schizophrenia
ClinicalTrials.gov study NCT06653829. IPD Sharing: YES. Countries: 1. Publications: 7.
Sparking Potential, Revealing Infant Neurocognitive Traits
ClinicalTrials.gov study NCT07390136. IPD Sharing: NO. Countries: 1. Publications: 1.
SPARK- a Digital Platform to Improve Self-management of Gestational Diabetes
ClinicalTrials.gov study NCT05348863. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Respiratory Mechanics in Supine and PARK-bench Positions (SPARK)
ClinicalTrials.gov study NCT06448988. IPD Sharing: NO. Countries: 1. Publications: 2.
Lichen speciation is sparked by a substrate requirement shift and reproduction mode differentiation
Open the record for dataset details and reuse information.
Models, Simulations, Measurements, and Analysis for Modeling and Simulating Apache Spark Streaming Applications
<p>Palladio component models, simulations results, measurement data, and R analysis script for the publication: Modeling and Simulating Apache Spark Streaming Applications.</p> <p>Symposium on Software Performance (SSP16)</p>
Figure 2 from: Rasoloariniaina JR, Ganzhorn JU, Riemann JC, Raminosoa N (2016) Water quality and biotic interaction of two cavefish species: Typhleotris madagascariensis Petit, 1933 and Typhleotris mararybe Sparks & Chakrabarty, 2012, in the Mahafaly Plateau groundwater system, Madagascar. Subterranean Biology 18: 1-16. https://doi.org/10.3897/subtbiol.18.8321
Figure 2 - Significant relationships between the abundance of Typhleotris madagascariensis and Typhleotris mararybe and water characteristics.
Datasets for SPARKS (Splicing Profile Analysis using RBP KD/KO Signatures)
<p>Dataset for SPARKS, including library and examples</p>
SPARK: Safety Study of Pradaxa in Atrial Fibrillation Patients by Regulatory Requirement of Korea
ClinicalTrials.gov study NCT01774370. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.