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38 results for “IBM”
Finalist in IBM + ETH EcoHackathon XPRIZE competition PRESENTATION
<p>The <strong>AI+Environment Hackathon</strong>, co-organized by the AI Center and GainForest, was a two-week competition (May 17th – June 2nd) aimed at leveraging machine learning to generate actionable environmental insights. Participants had the unique opportunity to contribute to the prestigious XPRIZE competition while addressing real-world challenges using environmental data gathered from the Amazon rainforest, provided by GainForest.</p> <p>Key highlights of the hackathon included:</p> <ul> <li><strong>Mentorship:</strong> Participants were guided by a team of ecologists and technical experts to develop innovative solutions.</li> <li><strong>Recognition:</strong> Outstanding submissions were featured in the XPRIZE finals in July, included in an academic paper, and showcased in a poster session held at the AI Center.</li> <li><strong>Skill Certification:</strong> All participants received a certificate recognizing their technical contributions and skills.</li> </ul> <p>Participants tackled real-world challenges using data-driven methods, combining environmental science with machine learning. The event encouraged collaboration and hands-on problem-solving, culminating in a closing ceremony and poster session on June 7th at the AI Center.</p> <p>With coding skills as a prerequisite, the hackathon not only fostered innovation but also created opportunities for participants to engage with leading experts, build meaningful solutions, and contribute to sustainability efforts in the Amazon rainforest. Those with advanced skills could also participate as mentors, coaching teams throughout the competition.</p>
Dataset: International Business Machines Corporation (IBM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Scalasca analysis report of the ASCI Sweep3D benchmark on 294,912 processes in virtual-node mode on IBM Blue Gene/P with manually annotated iterations
<p>A Cube3 performance analysis report written by the Scalasca parallel analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 294,912 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH. The measurement includes system topology information and manual annotations of the twelve iterations.</p>
IBM (Meganyctiphanes norvegica and Thysanoessa raschii) -- DATASETS
<p>In this file are found the datasets used for the following study:</p> <p>Individual‐based modeling explains the contrasted seasonality in size, growth, and reproduction of the sympatric Arctic (Thysanoessa raschii) and Nordic krill (Meganyctiphanes norvegica) in the St. Lawrence Estuary, eastern Canada</p> <p>This study was published on 2018-09-14 (doi: <a href="https://doi-org.acces.bibl.ulaval.ca/10.1002/lno.11032">10.1002/lno.11032</a>).</p> <p>The file MN_L&O_results.Rdata corresponds to the dataset for the<em>Meganyctiphanes norvegica </em>species, and the file TR_L&O_results.Rdata corresponds to the dataset for the <em>Thysanoessa raschii </em>species.</p>
IBM Watson's NLP model for annotating potato literature.
<p>Archived files(.zip) of the IBM Watson's domain specific supervised NLP model for annotating potato literature. Created with the help of <a href="https://www.ibm.com/watson/services/knowledge-studio/">Watson Knowledge Studio</a> </p>
Dataset Related to "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller"
<p>Dataset (and programs used to create it) for the publication "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller":</p> <p>Hannes Tröpgen, Mario Bielert, and Thomas Ilsche. 2023. Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller. In Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23), April 15–19, 2023, Coimbra, Portugal. ACM, New York, NY, USA, 10 pages. <a href="https://doi.org/10.1145/3578244.3583729">https://doi.org/10.1145/3578244.3583729</a></p> <p>Find additional descriptions of the data in the included readme files.</p> <p> </p> <p>This work is supported in part by the German National High Performance Computing (NHR@TUD).<br> The authors are grateful to the Center for Information Services and High Performance Computing at TU Dresden for providing the Power9 Systems used in the measurements and the support during them.</p>
Adapting ADCI Windows Desktop Graphical User Interface to ADCI-HT on IBM BG/Q.
<p>Supplementary Figure 1. <strong>Adapting ADCI Windows Desktop Graphical User Interface to ADCI-HT on IBM BG/Q. </strong>Data flow diagram illustrating the steps required to perform metaphase image processing tasks using A) Windows ADCI, and B) BG/Q ADCI (ADCI-HT) software platforms.</p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
IBM 1401 - MAINFRAME - Date: 1959
IBM 1401 Mainframe - CPU Model by: Breno Valli Museu do Computador Collection Created with Cinema 4D - Maxon Visit now: www.museudocomputador.org.br 3D ARTIFACT PAID BY: Guilherme Vieira Dutra WANT YOUR NAME IN OUR COLLECTION? https://www.catarse.me/museudocomputador_3d?ref=project_link Source: Objaverse 1.0 / Sketchfab
[Data augmentation in a TTL] - Fictive dataset (27.5M) with up to 5k reactions per template // (13'953 template extracted from USPTO-FULL IBM version)
<p>Full generated fictive dataset, containing 27.5M reactions with up to 5000 reactions per radius 1 reaction template (13'953 reaction templates from USPTO-full, IBM version).</p> <p>Title of the manuscript:</p> <p>"Data augmentation in a Triple Transformer Loop retrosynthesis model"</p> <p>Abstract: </p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div dir="auto"> <div>Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits its usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM®P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM+R by TTL transformer T3. We generated up to 5,000 reactions per template, resulting in 27.5 million validated fictive reactions covering the chemical space of the original UPSTO dataset. To exemplify the use of this dataset, we show that a single-step retrosynthesis transformer model trained with a template equilibrated subset of 1,097,374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div></div>
Scalasca analysis report of the ASCI Sweep3D benchmark on 65,536 processes in virtual-node mode on IBM Blue Gene/P
<p>A Cube3 performance analysis report written by the Scalasca Trace Analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 65,536 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH.</p>
Scalasca analysis report of the ASCI Sweep3D benchmark on 294,912 processes in virtual-node mode on IBM Blue Gene/P
<p>A Cube3 performance analysis report written by the Scalasca parallel analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 294,912 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH.</p>
Sustainability-3225848 IBM SPSS survey raw data
<p>The dataset represents 183 validated survey answers analysed in the paper Sustainability-3225848<br><br>The data was analysed using IBM SPSS Statistics vs 29</p>
Torre IBM Argentina Printable
La Torre IBM forma parte del conjunto Catalinas Norte, en el barrio de Retiro, en Buenos Aires, Argentina. Fue diseñada en 1978 por el Estudio Mario Roberto Álvarez y Asociados, y puede notarse en ella una similitud fuerte con los Edificios Olivetti (1972, de Egor Eiermann) en Fráncfort del Meno, Alemania, especialmente por el hecho de basarse en un apeo estructural. El edificio de IBM Argentina se inauguró en 1983, para alojar la sede central de la empresa en Argentina y fue el más nuevo del conjunto Catalinas Norte hasta la construcción de las torres Catalinas Plaza y Alem Plaza en 1995. Source: Objaverse 1.0 / Sketchfab
Study of Arimoclomol in Inclusion Body Myositis (IBM)
ClinicalTrials.gov study NCT02753530. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Framework for performing experiments on IBM Quantum Computers
<p>The data used in my B.Sc. thesis.</p>
Dataset - results of IBM of an evolving metacommunity
<p>Datasets and scripts corresponding to results of the IBM of an evolving metacommunity (https://doi.org/10.5281/zenodo.2280982) as shown in the paper: ''Regional neutrality evolves through local adaptive niche evolution" (www.pnas.org/cgi/doi/10.1073/pnas.1808615116).</p>
Cytoscape session for the potato knowledge graph extracted with IBM Watson's supervised NLP model
<p>2 cytoscape session files (.cys) representing the genotypic-phenotypic knowledge networks retrieved from scientific literature using IBM Watson. Input to these files were the following: </p> <ul> <li>cytoscapeSession_trainingSet: A training set of 34 full-test articles about potato flesh color</li> <li>cytoscapesession_testSet: A testing set of a 4023 abstracts from PubMed.</li> </ul> <p> </p>
Phase III Trial of Sirolimus in IBM
ClinicalTrials.gov study NCT04789070. IPD Sharing: NO. Countries: 3. Publications: 1.
Merged HLS2 (L30), ERA5-Land inputs and sample predictions of land surface temperature for the IBM granite-geospatial-land-surface-temperature model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and ERA5-Land data following the UTM CRS:WGS84. It has been assembled for predicting land surface temperature with a fine-tuned granite geospatial foundation model developed by IBM Research. In addition, we include sample predictions of land surface temperature derived from this model. Please see https://huggingface.co/ibm-granite/granite-geospatial-land-surface-temperature for more information on data preparation and model use.</p> <p><strong>HLS:</strong></p> <p>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <h4><strong>ERA5-Land:<br></strong></h4> <p>Copernicus Climate Change Service, Climate Data Store, (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p> <h4><strong>LST-predictions:</strong></h4> <p>These predictions of land surface temperature are derived from the IBM granite-geospatial-land-surface-temperature model and have been made available for Abidjan, Côte d’Ivoire and Johannesburg, South Africa for the period 2013-2023. </p> <h4>Attribution</h4> <p>Copernicus programme:</p> <p>Contains modified Copernicus Climate Change Service information [2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Data</strong></p> <p>Muñoz Sabater, J., Comyn-Platt, E., Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N., Cagnazo, C., Cucchi, M. (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p>
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.