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2,863 results for “Challenge”
Investing in Open Science infrastructure and repositories, shared repositories and Idea Challenge 2020-2021
<p>Recording of the OR2020 virtual session that includes:</p> <ul> <li>Investing in Open Science infrastructure and repositories: How IOI and SCOSS can help<strong> </strong>– Vanessa Proudman, SPARC Europe</li> <li>Shared repositories: building a multi-tenancy repository service at the British Library – Sara Gould, the British Library</li> <li>A year long Idea Challenge: the new beginning. Kicking-off Idea Challenge 2020-2021, for more information, please visit the <a href="https://or2020.sun.ac.za/a-year-long-idea-challenge/">Idea Challenge page</a>.</li> </ul>
GECCO Industrial Challenge 2015 Dataset: A heating system dataset for the 'Recovering missing information in heating system operating data' competition at the Genetic and Evolutionary Computation Conference 2015, Madrid, Spain
<p>Dataset of the 'Industrial Challenge: Recovering missing information in heating system operating data' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 11th-15th 2015, Madrid, Spain</p> <p> </p> <p>The task of the competition was to recover (impute) missing information in heating system operation time series'.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of heating system operational time series with missing values</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, A. Fischbach, C. Schlitt, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Major German heating systems supplier (S. Moritz)</p> <p> </p> <p> </p> <p>Industrial Challenge: Recovering missing information in heating system operating data</p> <p> </p> <p>The Industrial Challenge will be held in the competition session at the Genetic and Evolutionary Computation Conference. It poses difficult real-world problems provided by industry partners from various fields. Highlights of the Industrial Challenge include interesting problem domains, real-world data and realistic quality measurement</p> <p>Overview</p> <p>In times of accelerating climate change and rising energy costs, increasing energy efficiency and reducing expenses becomes a high priority goal for businesses and private households alike. Modern heating systems record detailed operating data and report this data to a central system. Here, the operating data can be correlated and analyzed to detect potential optimization opportunities or anomalies like unusually high energy consumption. Due to various difficulties this data might be incomplete which makes accurate forecasting even harder.</p> <p>Goal of the GECCO 2015 Industrial Challenge is to develop capable procedures to recover missing information in heating system operating data. Adequate recovery of the missing data enables more accurate forecastings which allow for intelligent control of the heating systems, and therefore contributes to a positive energy balance and reduced expenses.</p> <p> </p> <p><strong>Submission deadline:</strong><br> June 22, 2015</p> <p><strong>Official Webpage:</strong><br> <a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2015/">www.spotseven.de/gecco-challenge/gecco-challenge-2015/</a></p> <p> </p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Figure 1 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Figure 1 Main results of the World Café on experts' opinion to different aspects and challenges in research data management. Shown are the five stations ('Community Perspective', 'Reflection', 'Technology', 'Society & Values', and 'Personal Objectives') and the main points raised and discussed by the participants during the sessions.
Supplementary material 1 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Programm FDM-Expert*innentreffen 18.06.2019 (german)
Supplementary material 2 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Forschungsdatenmanagement mit NFDI: Wie sind wir vorbereitet? Wie partizipieren wir?
Supplementary material 4 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Photographs of notes from the discussions in the Leibniz Sessions
The political and institutional challenges facing the crisis of water resources and water and sanitation services in Chile (in Spanish)
<p>Talk (in Spanish) with Alex Caldera, University of Guanajuato, Mexico, and Jose Esteban Castro, National Council of Scientific and Technical Research (CONICET), Argentina and Newcastle University, UK. In this issue we feature Hugo Maturana Aguilar, President of the Workers Union of Valparaiso’s Company of Sanitary Works (ESVAL S.A.), and President of the Executive Directorship, National Federation of Workers of Sanitary Works (FENATRAOS), Chile, who talks about the challenges and opportunities facing water resources and water and sanitation services in Chile. The talk also focuses on the crisis triggered in the city of Osorno in July 2019, when the city was left without water supply for over two weeks by failures of the privatized Los Lagos Company of Sanitary Services (ESSAL), owned by the French multinational Suez.</p> <p>This podcast is part of the Voices for Water Series: http://waterlat.org/public-engagement/podcasts/.</p> <p>The podcast is part of the activities taking place within the Cooperation Agreement between the WATERLAT-GOBACIT Network and the Public Services International (PSI) (http://waterlat.org/projects/cooperation-agreement-with-the-psi/ ), and the collaboration with the Confederation of Workers of Water, Sanitation, and Environment Services of the Americas (CONTAGUAS) (http://www.contaguas.org/).</p> <p> </p> <p> </p>
Perspectives and Challenges in the Analysis of Prison Systems Data: A Systematic Mapping
<p>This is a list of 207 selected papers from a systematic mapping performed to analyze existing evidence on prison systems original data from peer-reviewed studies published between 2000 and 2019.</p>
A study of Mobile Technology Capabilities and Challenges in Library Service Delivery
<p>Mobile Technology has provided faster access to and delivery of information and other related services; it is also challenging the libraries and information centers to change round, modify and upgrade their services approach by implementing the new technological changes. Nowadays all mobile devices are gradually becoming an integral part of all and sundry thereby redirecting connectivity and interactions globally with dynamic feature compare to the static state of the computer technology. From the study, Mobile Technology capabilities will greatly support all forms of information access, storage, transmission etc. and provide help to researchers and users of research content to the strengthening of their relationship and providing enhanced user oriented services to existing users. The challenges facing mobile technology were also identified with a view of recommending to developers needed areas in satisfying local users. The study discusses on the capabilities and challenges of mobile technology in general services delivery</p>
Data and Intermediate Queries for: Evaluating institutional open access performance: Methodology, challenges and assessment
<p>This package provides the publicly sharable data for two related articles, alongside the key processing steps used to generate it.</p>
Emerging challenges for sustainable development and forest conservation in Sarawak, Borneo
<p>The forests of Borneo—the third largest island on the planet—sustain some of the highest biodiversity and carbon storage in the world. The forests also provide vital ecosystem services and livelihood support for millions of people in the region, including many indigenous communities. The Pan-Borneo Highway and several hydroelectric dams are planned or already under construction in Sarawak, a Malaysian state comprising part of the Borneo. This development seeks to enhance economic growth and regional connectivity, support community access to services, and promote industrial development. However, the implications of the development of highway and dams for forest integrity, biodiversity and ecosystem services remained largely unreported.</p> <p>We assessed these development projects using fine-scale biophysical and environmental data and found several environmental and socioeconomic risks associated with the projects. The highway and hydroelectric dam projects will impact 32 protected areas including numerous key habitats of threatened species such as the proboscis monkey (<i>Nasalis larvatus</i>), Sarawak surili (<i>Presbytis chrysomelas</i>), Bornean orangutans (<i>Pongo pygmaeus</i>) and tufted ground squirrel (<i>Rheithrosciurus macrotis</i>). Under its slated development trajectory, the local and trans-national forest connectivity between Malaysian Borneo and Indonesian Borneo would also be substantially diminished. Nearly ~161 km of the Pan-Borneo Highway in Sarawak will traverse forested landscapes and ~55 km will traverse carbon-rich peatlands. The 13 hydroelectric dam projects will collectively impact ~1.7 million ha of forest in Sarawak.</p> <p>The consequences of planned highway and hydroelectric dams construction will increase the carbon footprint of development in the region. Moreover, many new road segments and hydroelectric dams would be built on steep slopes in high-rainfall zones and forested areas, increasing both construction and ongoing maintenance costs. The projects would also alter livelihood activities of downstream communities, risking their long-term sustainability.</p> <p>Overall, our findings identify major economic, social and environmental risks for several planned road segments in Sarawak— such as those between Telok Melano and Kuching; Sibu and Bintulu; and in the Lambir, Limbang and Lawas regions—and dam projects— such as Tutoh, Limbang, Lawas, Baram, Linau, Ulu Air and Baleh dams. Such projects need to be reviewed to ensure they reflect Borneo's unique environmental and forest ecosystem values, the aspirations of local communities and long-term sustainability of the projects rather than being assessed solely on their short-term economic returns.</p>
Figure 5 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 5. Maraenobiotus slovenicus sp. nov., line drawings, holotype female: (A) antennule, dorsal; (B) antenna; (C) mandible; (D) maxillule; (E) maxilla; (F) maxilliped.
Figure 8 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 8. Maraenobiotus slovenicus sp. nov., line drawings, allotype male: (A) antennule, dorsal; (B) Endp P2; (C) P3; (D) P4; (E) P5 and P6.
Figure 6 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 6. Maraenobiotus slovenicus sp. nov., line drawings, holotype female: (A) P1; (B) P2; (C) P3; (D) P4; (E) P5.
Figure 2 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 2. Maraenobiotus slovenicus sp. nov., SEM micrographs, paratype female 2: (A) habitus lateral; (B) cephalothoracic shield, lateral; (C) pleurons of free pedigerous somites, lateral; (D) first three urosomites, lateral; (E) anal somite and right caudal ramus, lateral; (F) antennule, lateral; (G) P3 and P4, lateral; (H) P5, lateral.
Data for "Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification" (ImRex)
<p>Repository containing the different experiments described in the manuscript titled: "Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification".</p> <p>Publication DOI: TBA</p> <p>Originally appeared as a preprint on bioRxiv: <a href="https://doi.org/10.1101/2019.12.18.880146">https://doi.org/10.1101/2019.12.18.880146</a>.</p> <p>Contains:</p> <ul> <li>Trained model files (.h5)</li> <li>Associated train and validation datasets for each model.</li> <li>Learning curves and evaluation metrics.</li> <li>Log files with training and data arguments (full training scripts are available in GitHub repository).</li> <li>Comparisons between different models.</li> <li>Complete raw and processed datasets (also available in the associated GitHub repository).</li> </ul> <p><strong>Please refer to the associated GitHub repository (<a href="https://github.com/pmoris/ImRex">https://github.com/pmoris/ImRex</a>) for more information on the directory structure and contents, as well as the scripts that generated these output files.</strong></p> <p><strong>Contents:</strong></p> <ul> <li> <p><code>data.zip</code>: Contains raw and preprocessed datasets. READMEs in subdirectory describe the data sources and preprocessing steps. Please refer to the associated GitHub repository for the specific scripts that generated these files. Note that the full training and test sets (i.e. containing both positive and negative examples) are stored separately for each model/CV iteration in the <code>models</code> archives.</p> </li> <li> <p><code>models-main.zip</code>: contains the trained models and evaluation metrics for the main different experiments described in the bash and pbs scripts in <code>./src/scripts/hpc_scripts</code>. Log files for the experiments outlined here can be found in <code>./src/scripts/hpc_scripts</code>.</p> </li> <li> <p><code>models-full.zip</code>: contains models that were trained on the complete VDJdb dataset without cross-validation, filtered on human TRB data, no 10x data and restricted to 10-20 (CDR3) or 8-11 (epitope) amino acid residues, with negatives that were generated by shuffling (i.e. sampling an negative epitope for each positive CDR3 sequence). One set of models uses downsampling to reduce the most abundant epitopes down to 400 pairs each, the other one does not use any downsampling. These models were also used for evaluating on the external Adaptive dataset, as outlined in <code>./src/scripts/evaluate/evaluate_adaptive.sh</code>, and the TRA subset of sequences (<code>./src/scripts/evaluate/evaluate_tra.sh</code>).</p> </li> <li> <p><code>models-decoyfit.zip</code>: contains models that were trained on true data, but evaluated on data where epitopes were replaced by decoys.</p> </li> <li> <p><code>models-padded-epitoperatio.zip</code>: contains a quick test of trained models (padded/interaction map) that use a different type of negative shuffling, see docstrings in <code>./src/processing/negative_sampler.py</code> for more info.</p> </li> <li> <p><code>models-repeat-local.zip</code>: contains a number of repeated runs from <code>models-main</code>, used to estimate variability in model performance for multiple identical runs.</p> </li> <li> <p><code>comparisons.zip</code>: contains comparison directories, each consisting of two or more model output directories, that contrast the performance metrics of the models. These outputs were generated by using the <code>./src/scripts/evaluate/visualize.py</code> script, or by using the oneliners in <code>./src/scripts/evaluate/visualise.sh</code>, which can operate on the entire comparisons directory at once.</p> </li> </ul> <p><strong>Note that any file paths described here are in reference to the associated GitHub repository (<a href="https://github.com/pmoris/ImRex">https://github.com/pmoris/ImRex</a>).</strong></p> <p><strong>Overview of different experiments:</strong></p> <ul> <li>Two main architectures were compared: the interaction map (or <code>padded</code>) CNN and a dual input CNN based on NetTCR (<code>nettcr</code>).</li> <li>Two different cross-validation strategies were used: a 5x repeated 5-fold CV (<code>repeated5fold</code>) and an epitope-grouped CV (<code>epitope_grouped</code>).</li> <li>The different dataset subsets are labelled as follows. Check the Makefile's <code>preprocess-vdjdb-aug-2019</code> command (and the underlying script <code>./src/scripts/preprocessing/preprocess_vdjdb.py</code>) for a more thorough overview of the different filtering options. <ul> <li><code>mhci</code>: only MHCI class presented epitopes.</li> <li><code>trb</code>: only TRB CDR3 sequences.</li> <li><code>tra</code>: only TRA CDR3 sequences.</li> <li><code>tratrb</code>: both types of CDR3 sequences.</li> <li><code>down</code>: moderate downsampling of most abundant epitopes to 1000 pairs.</li> <li><code>down400</code>: strong downsampling of most abundant epitopes to 400 pairs.</li> <li><code>decoy</code>: decoy epitope data.</li> <li><code>reg001</code>: regularization factor 0.01 (only for padded/interaction type models, fixed value)</li> </ul> </li> <li>Two different methods of generating negative TCR-epitope pairs were used: shuffling of positive pairs, i.e. sampling a single epitope from the positive pairs for each CDR3 sequence (<code>shuffle</code>), and sampling CDR3s from a reference repertoire (<code>negref</code>).</li> <li>The batch size is labelled as <code>b32</code> = a batch size of 32.</li> <li>The learning rate was always 0.0001 (<code>lre4</code>) or 0.001 (<code>lre3</code>).</li> </ul> <p> </p>
Challenges and Recommendations in DevOps Education: A Systematic Literature Review
<p>Over the last years, DevOps has gained more importance and attention from the software industry, given its role in enabling continuous software delivery. As a new area, DevOps has brought significant challenges for the academy, both in terms of research topics and teaching strategies. In this paper, we present a systematic literature review that aims to identify challenges and recommendations for teaching DevOps. Our findings show a total of 73 challenges and 85 recommendations organized into different seven categories from a total of 18 papers selected. We also discuss how existing recommendations address the challenges found in the study, thus contributing to the preparation and execution of DevOps courses. Finally, we investigate if challenges and recommendations are specific for teaching DevOps.</p>
An Experience Report About Challenges of Software Engineering as a Second Cycle Course
<p>The dynamism of the software development market has demanded increasingly qualified professionals with practical knowledge on the implementation of paradigms and methodologies in the development of projects. In this context, there are undergraduate courses in Software Engineering (SE) offered in the format of two cycles, to try to deliver to the market professionals who meet the demands of the sector. Therefore, it is important to know the challenges that arise in the teaching scenario and the emerging topics of SE, in order to provide students with an academic-professional training closer to the real scenario of the software industry. Thus, this article aims to identify the difficulties observed in an undergraduate course in SE and its emerging topics, in the form of two cycles. For this, a survey with professors from a two-cycle SE course was carried out. Through a quantitative and qualitative analysis of the results, it was possible to identify some main challenges faced by professors. This type of analysis has proved to be useful to identify opportunities for improvement in the academic-professional training of SE students and to provide the software industry with more qualified professionals.</p>
Challenges in Agile Transformation Journey - A Qualitative Study (Presentation)
<p><em>Context</em>: The agile transformation of an organization is influenced by different factors that can hinder the initiative's success, such as lack of adaptation of agile practices to the organizational context, selection of agile alternatives without using appropriate criteria, and lack of knowledge of the associated challenges. Not all organizations properly recognize these factors. <em>Objective</em>: We aim to identify challenges that can impact the success of agile transformations initiatives. <em>Method</em>: We executed a qualitative study using thematic analysis. We interviewed key persons in charge of the agile journey in four Brazilian organizations. <em>Results</em>: We identified 26 challenges in five dimensions: People, Organizational Culture, Processes, Scalability, and Technology. Among the main challenges are inadequate training, communication difficulties, a culture conflicting with agile values, incompatible organizational structure, and lack of a process to guide the transition. <em>Conclusion</em>: The results suggest urgency in the treatment of agile adoption as a contextualized project for organizational improvements.</p>
ScienceDex guides
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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.