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830 results for “industry”
Dataset for "Architectural Security Weaknesses in Industrial Control Systems: An Empirical Study Based on Security Advisories' Vulnerability Reports"
<p>Supplementary artifacts to "<em>Architectural Security Weaknesses in Industrial Control Systems (ICS): An Empirical Study based on Disclosed Software Vulnerabilities</em>"</p> <p>Published in the Proceedings from the 2019 IEEE International Conference on Software Architecture (ICSA)</p> <p>Package Contains:</p> <p>- Raw output showing Components, CAWEs, and CVEs per report</p> <p>- Frequency Data (# of reports) for those concerns</p> <p>- ICS Component - Term Dictionary </p> <p>- HTML versions of reports studied in paper</p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</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>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
Dataset for the paper "Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943
<p>This repository contains the data needed to reproduce the results in: </p> <p>Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943</p> <p>The dataset was also used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of data are specified in the dataset (under tab "References")</p>
Agile Accelerator Program: From Industry-Academia Collaboration to Effective Agile Training
<p>The agile accelerator program takes place in a Brazilian technology park, as a collaboration between a university and a world-renowned technology company, specialized in agile development and consulting. This partnership has 8-year long with the main goal of preparing undergraduate students to work in high-performance agile teams. This partnership created a culturally rich environment for student learning while influencing other companies to follow the same initiative within this technology park. We conducted a Case Study aiming to characterize this partnership (explaining how it works) and the resulting program, understanding the benefits to the program students. Our results point out the importance of the kind of partnership that provides an immersive learning environment to students, where students can learn empirically, with real projects and real stakeholders and how important it was for the program's former students to enter the job market. This successful enhanced students' training program on agile software development through the blending of culture between institutions can be of inspiration to those interested in aiming to bridge the gap between academia and industry.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</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>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, 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> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 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.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
FIG. 15. — A in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 15. — A, archaeological shells with use-wear traces on the edge (Cuccuru s'Arriu, Cabras, Italy); B, use-wear traces related to contact with plant matter. Scale bars: A, 5 cm; B, 100 µm.
FIG. 13 in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 13. — Archaeological shells with use-wear traces on the ventral face (Cuccuru s'Arriu, Cabras, Italy) related to contact with an indeterminate mineral matter. Scale bars: A, 5 cm; B, 100 µm.
FIG. 14. — A in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 14. — A, archaeological shells with use-wear traces on the edge (Cuccuru s'Arriu, Cabras, Italy); B, valve with mineral colouring residue on the ventral face, near the upper edge; C, indeterminate use-wear traces; D, valve with use-wear traces (E-H) related to contact with plant matter. Scale bars: A, D, 5 cm; B, C, E-H: 100 µm.
FIG. 6. — A in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 6. — A, working mode; B, results obtained; C, D, experimental shell surface used to process rushes (separation of the stems; 15 minutes); E, working conditions; F, results obtained; G, H, experimental shell surface used to process flax (crushing; 15 minutes). Scale bars: 100 µm.
FIG. 10. — A in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 10. — A, Archaeological shell with use-wear traces (C-D) and red-dye traces (ochre?) (B) on the dorsal face (Cuccuru s'Arriu, Cabras, Italy) related to contact with a mineral matter, clay. Scale bars: A, 5 cm; B, C, D, 100 µm.
FIG. 5. — A, B in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 5. — A, B, working conditions (A, 45° inclination; B, 135° inclination); C, D, experimental shell surface used to process dry hide (scraping with the use of ochre; 15 minutes); E, F, experimental shell surface used to process boxwood (scraping; 15 minutes); G, H, experimental shell surface used to process basswood (scraping; 15 minutes). Scale bars: 100 µm.
FIG. 9. — A in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 9. — A, Archaeological shell with use-wear traces of suspension (Cuccuru s'Arriu, Cabras, Italy); B-C, use-wear traces localized on the edge of natural perforation (B) and on the hinge (C). Scale bars: A, 5 cm; B, C, 100 µm.
FIG. 4. — A, B in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 4. — A, B, experimental shell surface used to process dry hide (softening; 15 minutes); C, D, working conditions (C, 45° inclination; D, 135° inclination); E, F, experimental shell surface used to process dry hide (scraping). Scale bars: 100 µm.
FIG. 1. — A, B in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 1. — A, B, Geographical data; C, Location of the site of Cuccuru s'Arriu (red point) and indication of beaches near the site with Glycymeris valves (Tharros West, San Giovanni di Sinis, Su Maimoni and Pesaria).
FIG. 12. — A in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 12. — A, archaeological shell with use-wear traces (B-D) on the ventral face (Cuccuru s'Arriu, Cabras, Italy) related to contact with a mineral matter: utilization as a container for mixing mineral substances. Scale bars: A, 5 cm; B, C, D, 100 µm.
FIG. 3. — A-D in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 3. — A-D, micro-polishes identified on the dorsal face of Glycymeris valves with different microtopography, texture, fabric and extension:A, absence of micropolish; B-D, presence of micro-polish becoming gradually more marked; E, F, scratches identified on the dorsal face of valves. Scale bars: 100 µm.
FIG. 8. — A, B in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 8. — A, B, use of shells as containers (mixing the crushed ochre with a sticky substance; 1 hour); C, D, experimental shell surface. Scale bars: 100 µm.
FIG. 16 in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 16. — Archaeological shells with use-wear traces on the edge (Cuccuru s'Arriu, Cabras, Italy). A, valve with use-wear traces (B, C, D) related to contact with a mineral matter, clay; E, Valve with use-wear traces (F, G, H) related to the contact with indeterminate mineral matter. Scale bars: 100 µm.
FIG. 7. — A, B in The shell industry in Final Neolithic societies in Sardinia: characterizing the production and utilization of Glycymeris da Costa, 1778 valves
FIG. 7. — A, B, experimental shell surface used to process clay with the dorsal face of valves (smoothing; 15 minutes); C, D, experimental shell surface used to process clay with the edge of valves (smoothing; 15 minutes); E-G, experimental shell surface used to process clay with the dorsal face of valves (smoothing; 1 month). Scale bars: 100 µm.
FIG. 5. — Undated female red deer upper left canine from Abri des Autours. A in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 5. — Undated female red deer upper left canine from Abri des Autours. A, general view; B, detail of perforation; C, detail of the crown with remaining traces of enamel (arrow). Scale bars: A, 10 mm; B, C, 1 mm.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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