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.
1,724
datasets available to search
ShareScore release 0.9.0
Dataset results
1,724 results for “ESCALATOR”
ESCALATOR - Stakeholder map data workflow
<p>The stakeholder map project aims to collect and share data on Digital Humanities (DH), Computational Social Sciences (CSS) and related activities and initiatives in South Africa. This data includes information about South African researchers, projects, publications, tools, datasets, academic programmes, training events, learning materials, and more. The aim is to provide deeper insight into the breadth of activities in this area, facilitate enhanced networking and collaboration, and support the optimal use of resources. The stakeholder map will, for example, support researchers looking for collaborators, help potential students to identify undergraduate and postgraduate training programmes, and highlight gaps and opportunities to funders and institutions.</p> <p>The initial design of the data pipeline and workflow for data visualisation has been completed. The pipeline is primarily based on open-source software and platforms often used in the open science community. Development is currently under way. Data will be captured via Google Forms and manipulated using R scripts, available on GitHub and archived in Zenodo. Interactive visualisations will be published on the ESCALATOR website. These visualisations include a [Shiny app](https://shiny.rstudio.com/) that will allow the community to explore data through a web interface and a [Kumu network visualisation](https://kumu.io/). Research articles can be added to an [open collection in Zotero](https://www.zotero.org/groups/3866799/dhcssza) to facilitate easy access to publications from the South African community.</p> <p><br> This diagramme shows the high-level workflow. We anticipate the diagramme will be updated as design and development progresses to incorporate lessons learned and feedback from the community.</p>
Industrial Benchmark Dataset for Customer Escalation Prediction
<p>This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. </p> <p>The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years.</p> <p>The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges is the optimal fusion of enterprise and log based features for the prediction task. Thereby, interpretability of designed prediction models should be ensured in order to have practical relevancy. </p> <p><strong>Supporting software</strong></p> <p>Kindly use the corresponding <a href="https://github.com/annguy/customer-sentiment-monitor">GitHub repository</a> (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. </p> <p> </p> <p><strong>Citation and Contact</strong><br> </p> <p>If you use this dataset please cite the following publication:</p> <p><br> </p> <pre><code>@ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}}</code></pre> <p> </p> <p>If you would like to get in touch, please contact an.nguyen@fau.de.<br> </p>
Geodetic anomaly detection and analysis in the Campi Flegrei caldera (Italy) deformation pattern of the 2021-2023 escalating unrest phase
<p>Data used within the manuscript: "<strong><span>First evidence of a geodetic anomaly in the Campi Flegrei caldera (Italy) ground deformation pattern revealed by DInSAR and GNSS measurements during the 2021-2023 escalating unrest phase</span>"</strong></p> <p> </p> <p>Archive content:</p> <ul> <li><code>DTSLOS_CNRIREA_20150325_20231021_FB9K</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from ascending orbits (Track 44) over Campi Flegrei caldera in the 20150325 - 20231021 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>DTSLOS_CNRIREA_20150324_20231020_UJBI</code>: Line of Sight displacement time series retrieved by applying the P-SBAS algorithm to Sentinel-1 data set acquired from descending orbits (Track 22) over Campi Flegrei caldera in the 20150324 - 20231020 interval. Data format is according to the <a href="https://gitlab.com/epos-tcs-satdata/doc/-/blob/main/TCS_SATD_Product_Description.md#los-displacement-time-series-dtslos" target="_blank" rel="noopener noreferrer">EPOS specification</a>.</li> <li><code>Campi_Flegrei_GNSS_Weekly_Timeseries</code>: Weekly displacement time series of Campi Flegrei caldera GNSS network from 2016 to 2023.</li> </ul>
Colletia paradoxa (Spreng.) Escal. (BR0000024858540)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Colletia paradoxa (Spreng.) Escal. (BR0000024858557)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Colletia paradoxa (Spreng.) Escal. (BR0000024858564)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
ESCALATOR Timeline
<p>The infographic was developed to show highlights of each of the five phases of the ESCALATOR programme between its inception in 2020 and the current phase in May 2024. The image is used in the close-out report that summarises activities and outputs for the period December 2020 - May 2024.</p> <p><a href="https://escalator.sadilar.org">ESCALATOR</a> is a programme developed by the <a href="https://sadilar.org">South African Centre for Digital Language Resources</a> (SADiLaR). ESCALATOR aims to support the development of an active and inclusive community of practice in Digital Humanities and Computational Social Sciences in South Africa.</p> <p>SADiLaR is supported by the Department of Science and Innovation as part of the South African Research Infrastructure Roadmap initiative.</p>
Figure 7 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 7. Separated (a) and aggregated (b) comparison of the distribution areas of the beetle and its major host plants based on von Raab-Straube's (2014) study.
Figure 5 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 5. Seasonal activity of Lamprodila festiva as a function of Köppen-Geiger climate zones (Peel et al. 2007) based on the online observation records. The grey zones indicate the centre of seasonal flight activity (15 -15 percent intervals from the arithmetic mean of all observation records).
Figure 4 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 4. Different European propagation zones of Lamprodila festiva and the main directions of their escalation
Figure 3 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 3. Occurrence data, distribution, and the theoretical spreading of Lamprodila festiva in Europe based on the data of Table 1. Explanation: the hatched area represents the assumed distribution area in the given period.
Figure 2 in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 2. The distribution of the content of publications on Lamprodila festiva as a function of time.
Figure 1. A in A story of becoming a horticultural threat, cypress jewel beetle Lamprodila festiva (Coleoptera, Buprestidae): analytical approach of its European escalation based on bibliographical sources
Figure 1. A spherical coordinate system on Earth is used for calculating the propagation vectors (a). The projection method of the observation points around the hot points as the centres of the new reference coordinate system (b).
Figure 3 in The study of exposure times and dose-escalation of tick saliva on mouse embryonic stem cell proliferation
Figure 3. Effect of D. marginatus SGE (0-160 µg/ml) on mouse embryonic stem cell proliferation and viability. Values represent relative fold change of cell viability normalized to untreated negative control. The Geisser–Greenhouse correction and Dunnett´s test on multiple comparison were used. All experiments have the P value <0.05, on three different time laps. The results are mean ± standard deviation (SD) from a representative experiment carried out in triplicate and were seeded in equal amount in 3 different 96-well cultured plates.
Figure 2 in The study of exposure times and dose-escalation of tick saliva on mouse embryonic stem cell proliferation
Figure 2. Effect of R. bursa SGE (0-160 µg/ml) on mouse embryonic stem cell proliferation and viability. Values represent relative fold change of cell viability normalized to untreated negative control. The Geisser–Greenhouse correction and Dunnett´s test on multiple comparison were used. The results are mean ± standard deviation (SD) from a representative experiment carried out in triplicate and were seeded in equal amount in 3 different 96-well cultured plates.
Suppression force-fields and diffuse competition: Competition de-escalation is an evolutionarily stable strategy
<p><span>Competition theory is founded on the premise that individuals benefit from harming their competitors, which helps them secure resources and prevent inhibition by neighbours. When multiple individuals compete, however, competition has complex indirect effects that reverberate through competitive neighbourhoods. The consequences of such "diffuse" competition are poorly understood. For example, competitive effects may dilute as they propagate through a neighbourhood, weakening benefits of neighbour suppression. Another possibility is that competitive effects may rebound on strong competitors, as their inhibitory effects on their neighbours benefit other competitors in the community. Diffuse competition is unintuitive in part because we lack a clear conceptual framework for understanding how individual interactions manifest in communities of multiple competitors. Here, I use mathematical and agent-based models to illustrate that diffuse interactions—as opposed to direct pairwise interactions—are likely the dominant mode of interaction among multiple competitors. Consequently, competitive effects may regularly rebound, incurring fitness costs under certain conditions, especially when kin-kin interactions are common. These models provide a powerful framework for investigating competitive ability and its evolution and produce clear predictions in ecologically realistic scenarios.</span></p>
A Dose Escalation Study of a Combination Antihypertensive Drug in the Treatment of Various Groups of Patients Who do Not Respond to Single Drug Treatment of Their High Blood Pressure
ClinicalTrials.gov study NCT00791258. IPD Sharing: YES. Countries: 1. Publications: 2.
Phase 1/2 Dose Escalation and Efficacy Study of Anti-CD38 Monoclonal Antibody in Patients With Selected CD38+ Hematological Malignancies
ClinicalTrials.gov study NCT01084252. IPD Sharing: YES. Countries: 17. Publications: 2.
Study Estimating the Clinical Difference Between 300 mg and 150 mg of Secukinumab Following Dose Escalation to 300 mg in Patients With Ankylosing Spondylitis
ClinicalTrials.gov study NCT03350815. IPD Sharing: YES. Countries: 1. Publications: 1.
First in Human Testing of Dose-escalation of SAR440234 in Patients With Acute Myeloid Leukemia, Acute Lymphoid Leukemia and Myelodysplastic Syndrome
ClinicalTrials.gov study NCT03594955. IPD Sharing: YES. Countries: 2. Publications: 1.
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.