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Change in gender roles as a factor in gender participation and empowerment in the oil mining industry: A case of Lokichar, Turkana county, Kenya
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Data from: Brokering the core and the periphery: creative success and collaboration networks in the film industry
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Exploring the effect of industrial agglomeration on income inequality in China
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Cannabis industry businesses, organizations, and individuals that lobby in Colorado
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Fig. 16 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 16. Species of the order Tetraodontiformes, family Balistidae, (A) Balistes capriscus MPEG 33692, 259 mm TL, (B) Balistes vetula not cataloged, 180 mm TL, family Monacanthidae, (C) Aluterus heudelotii, AZUSC 5494, 350 mm TL, (D) Aluterus monoceros MPEG 35756, 481 mm TL, (E) Aluterus scriptus not cataloged, 360 mm TL, (F) Cantherrines macrocerus not cataloged, 220 mm TL, family Ostraciidae, (G) Acanthostracion polygonius MPEG 35154, 161 mm TL, (H) Acanthostracion quadricornis MPEG 35174, 276 mm TL, (I) Lactophrys trigonus AZUSC 5188, 180 mm TL, family Tetraodontidae, (J) Lagocephalus laevigatus MPEG 35175, 340 mm TL, (K) Sphoeroides dorsalis not cataloged, 60 mm TL, family Diodontidae, (L) Chilomycterus reticulatus MPEG 35614, 206 mm TL, (M) Chilomycterus spinosus MPEG 35562, 138 mm TL, (N) Chilomycterus antillarum MPEG 35185, 182 mm TL.
Fig. 15 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 15. Species of the Ordem Batrachoidiformes, family Batrachoididae, (A) Amphichthys cryptocentrus not cataloged, 240 mm TL, (B) Porichthys plectrodon MPEG 35664, 43 mm TL, (C) Thalassophryne nattereri not cataloged, 140 mm TL, Ordem Scombriformes, family Scombridae, (D) Acanthocybium solandri not cataloged, 450 mm TL, (E) Euthynnus alletteratus not cataloged, 390 mm TL, (F) Scomberomorus brasiliensis MPEG 35108, 509 mm TL, (G) Scomberomorus cavalla not cataloged, 420 mm TL, (H) Thunnus atlanticus not cataloged, 480 mm TL, Ordem Syngnathiformes, family Syngnathidae, (I) Hippocampus reidi, AZUSC 5388, 98 mm TL.
Fig. 14 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 14. Species of the order Pleuronectiformes, family Paralichthyidae, (A) Citharichthys arenaceus MPEG 35148, 109 mm TL, (B) Citharichthys macrops AZUSC 5119, 77 mm TL, (C) Cyclopsetta chittendeni MPEG 35119, 293 mm TL, (D) Etropus crossotus MPEG xxx mm TL, (E) Syacium papillosum MPEG 35559, 179 mm TL, family Achiridae, (F) Achirus declivis AZUSC 5461, 150 mm TL, (G) Achirus lineatus MPEG 35113, 173 mm TL, (H) Gymnachirus nudus not cataloged, 150 mm TL, (I) Trinectes paulistanus MPEG 35762, 190 mm TL, family Cynoglossidae, (J) Symphurus oculellus AZUSC 4935 4935, 119 mm TL, (K) Symphurus tesselatus MPEG 35503, 202 mm TL.
Fig. 2 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 2. Collection conducted by observers of the Centro de Pesquisa e Gestão de Recursos Pesqueiros do Litoral Norte do Brasil (CEPNOR), embarked on trawlers.
Fig. 5 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 5. Species of the order Anguilliformes, family Muraenesocidae, (A) Cynoponticus savanna MPEG 35777, 566 mm TL, family Congridae, (B) Paraconger guianensis MPEG 35216, 195 mm TL, (C) Rhynchoconger flavus MPEG 35746, 365 mm TL, family Nettastomatidae, (D) Hoplunnis macrura AZUSC 5670, 320 mm TL.
Fig. 10 in The bony fishes (Teleostei) caught by industrial trawlers off the Brazilian North coast, with insights into its conservation
Fig. 10. Species of the order Perciformes, family Chaetodontidae, (A) Chaetodon ocellatus MPEG 35121, 91 mm TL, (B) Chaetodon sedentarius not cataloged, 150 mm TL, order Perciformes, family Lutjanidae, (C) Lutjanus apodus MPEG 34520, 272 mm TL, (D) Lutjanus campechanus, AZUSC 5483, 330 mm TL, (E) Lutjanus cyanopterus not cataloged, 490 mm TL, (F) Lutjanus vivanus MPEG 35576, 283 mm TL, (G) Ocyurus chrysurus not cataloged, 260 mm TL, (H) Pristipomoides aquilonaris AZUSC 5186, 200 mm TL, (I) Rhomboplites aurorubens not cataloged, 330 mm TL, family Gerreidae, (J) Diapterus rhombeus MPEG 35191, 192 mm TL, family Haemulidae, (K) Haemulon aurolineatum not cataloged, 170 mm TL, (L) Haemulon carbonarium not cataloged, 260 mm TL, (M) Haemulon sp. MPEG 35708, 189 mm TL, (N) Orthopristis scapularis MPEG 35647, 185 mm TL.
Development and demonstration of the next generation of renewable energy-driven technologies for buildings and industrial processes heating and cooling - João Soares 6 year plan
<p>This figure illustrates the six years research plan and methods of João Soares, with the main goal to develop, evaluate and demonstrate the next generation of RES (solar, biomass or hybrid) driven technologies for heating and cooling (H/C) in buildings and industrial processes. </p>
Dataset used in Paper 1046 of the IFAC World Congress 2020 "Data Quality Assessment for System Identification in the Age of Big Data and Industry 4.0"
<p><br> The data used was obtained from a section of the lead zinc concentrator at the Mount Isa Mines in Queensland, Australia.The data collected for this investigation consists of two months of plant operation, collected at a a frequency of one minute. The historian’s interpolation routine is used to ensure the data is aligned. No special care was used to ensure that the data had any particular characteristics, other than that the plant was running. It was reported that during this period some step tests had been conducted. Forty-three variables were collected: for each of the PID controllers, setpoint, process value and output (SV/PV/MV) were recorded. The three analysers provide measure of iron, lead and zinc percentages.</p>
Figure 5 in Phorodon cannabis Passerini (Hemiptera: Aphididae), a newly recognized pest in North America found on industrial hemp
Figure 5. Phorodon humuli (Schrank). a) Alate vivipara photomicrograph. b) Antennal segments II–VI. c) Siphunculus. d) Head and antennal segment I (left side dorsum; right side venter). e) Cauda dorsum.
Figure 3. Phorodon cannabis Passerini. a in Phorodon cannabis Passerini (Hemiptera: Aphididae), a newly recognized pest in North America found on industrial hemp
Figure 3. Phorodon cannabis Passerini. a) Alate vivipara photomicrograph. b) Antennal segments II-VI. c) Siphunculus. d) Head and antennal segment I (left side dorsum; right side venter). e) Cauda dorsum. f) Enlargement of dorsal abdominal spatulate setae.
Raw OPC-UA Dataset of a Working Industry 4.0 Smart Factory (H-DA AutFab)
<p>This OPC-UA dataset was recorded in the AutFab, the fully automated Industry 4.0 learning factory of the University of Applied Sciences Darmstadt (Description of the AutFab, refer to: 10.1016/j.promfg.2017.04.023). Recording Date was the 2019-03-13. Five successful assemblies would be done during recording. The Dataset consists of 17.464 columns and 11.455 rows. Each column correspond to an OPC-UA node. The pre-transformation was limited to transpose the logformat: time, opcua-nodeid, value to time, opcua-nodeid 1, ..., opcua-nodeid N and align all events by time. A short analysis was carried out, which shows that only 1022 columns change more than once. This dataset contains no anomalies or errors. Usage must be requested.</p>
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>
Five years of pharmaceutical industry funding of patient organisations in Sweden: cross-sectional study of companies, patient organisations and drugs
<p><strong>Abstract</strong></p> <p><strong>Background:</strong> Many patient organisations collaborate with drug companies, resulting in concerns about commercial agendas influencing patient advocacy. We contribute to an international body of knowledge on patient organisation-industry relations by considering payments reported in the industry’s centralised ‘collaboration database’ in Sweden. We also investigate possible commercial motives behind the funding by assessing its association with drug commercialisation. </p> <p><strong>Methods:</strong> Our primary data source were 1,337 payment reports from 2014-2018. After extraction and coding, we analysed the data descriptively, calculating the number, value and distribution of payments for various units of analysis, e.g. individual companies, diseases and payment goals. The association between drug commercialisation and patient organisation funding was assessed by, first, the concordance between leading companies marketing drugs in specific diseases and their funding of corresponding patient organisations and, second, the correlation between new drugs in broader condition areas and payments to corresponding patient organisations.</p> <p><strong>Results: </strong>46 companies reported paying €6,449.224 (median €2,411; IQR €1,024-4,569) to 77 patient organisations, but ten companies provided 67% of the funding. Small payments dominated, many of which covered costs of events organised by patient organisations. An association existed between drug commercialisation and industry funding. Companies supported patient organisations in diseases linked to their drug portfolios, with the top 3 condition areas in terms of funding – cancer endocrine, nutritional and metabolic disorders; and infectious and parasitic disorders – accounting for 63% of new drugs and 56% of the funding.</p> <p><strong>Conclusion:</strong> This study reveals close and widespread ties between patient organisations and drug companies. Relatively few companies dominated the funding landscape by supporting patient organisations in disease areas linked to their drug portfolios. This commercially motivated funding may contribute to inequalities in resource and influence between patient organisations. The association between drug commercialisation and industry funding is also worrying because of the therapeutic uncertainty of many new drugs. Our analysis benefited from the existence of a centralised database of payments – which should be adopted by other countries too – but databases should be downloadable in an analysable format to permit efficient and independent analysis.</p>
Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective
<p>Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective<br> R. Maria del Rio-Chanona, Penny Mealy, Anton Picheler, Francois Lafond, J. Doyne Farmer<br> contact:<br> Results <br> The supply, demand, and total shocks at the industry and occupation level are in files:</p> <p>industry_variables_and_shock.csv<br> occupation_variables_and_shock.csv</p> <p>To reproduce our results we also include<br> The employment data between industries and occupations<br> industry_occupation_employment.csv<br> The classification of work activities<br> iwa_remotelabor_labels.csv<br> The essential score of industries at the NAICS 4d level<br> essential_score_industries_naics_4d_rev.csv<br> </p> <p>Update with respect to the previous version</p> <p>We have now included our code to reproduce our study from scratch.</p>
Additional Material: Virtual Humans in AR: Evaluation of Presentation Concepts in an Industrial Assistance Use Case
<p>Additional material to the evaluation on different presentation concepts of a virtual human in HMD-based AR</p>
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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.