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2,045 results for “planning”
Fig. 2 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 2. Cheilobarbus serra (Peters, 1864) (max. length: 350 mm) (A); adult Paradiplozoon sp. (B) and sclerites in attachment clamps (C, D) found on the gills; hamuli of Gyrodactylus sp. (E) and marginal hooks (F), and a pre-metamorphic stage of the copepod belonging to the Lernaeidae (G), both from the gills. Anterior (H) and posterior (I) ends of Rhabdochona sp. 1 (lateral view) from the intestine; (J) whole specimen of the Caryophyllidea. Scale bars: 10 μm (E, F); 100 μm (C, D, G, H, I); 500 μm (B); 1000 μm (J).
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning
<p>Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning</p> <p>Data contains shapefiles that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the <em>JOURNAL NAME</em> publication and the Zenodo dataset.</strong></p> <p>Iqbal, W., Head III, J. W., van der Bogert, C. H., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Slopes along Apollo EVAs: Astronaut experience as input for future mission planning. Acta Astronautica, 223, 184-196. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actaastro.2024.07.006" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.actaastro.2024.07.006</a></p> <p>Iqbal, W., Head, J. W., van der Bogert, C., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning [Data set]. In Acta Astronautica (Bd. 223, S. 184–196). Zenodo. <a href="https://doi.org/10.5281/zenodo.13790204" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13790204</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Structure</p> <p>-> File "Apollo_Traverses_Iqbal_24" - It includes six subfolders for each landing site that contain the shapefiles of traverses.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections? - Data Collected and Presented for the Experiments
<p>This dataset contains images collected (and processed) for the experiments of Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?" journal article, a work that defines a new path planning problem for UAVs - the Fast Inspection of Scattered Regions (FISR) - and introduces a novel method that deals with this problem - the multi-UAV Disjoint Areas Inspection (mUDAI) method. For the validation of the introduced methodology, two sets of real-world experiments were executed, one small-scale in Galatsi, Athens, were two mUDAI missions were depolyed, with two different optimization objectives for the data collection procedure (Mazimized Coverage Objective - MCO, and Balanced Coverage Objective - BCO), and one large scale in ZEP-Kissos, Thessaloniki, where a Coverage Path Planning (CPP) mission, and 2 mUDAI missions, one with a single and one with two UAVs, using both the MCO criterion for the data collection, were deployed. Regarding the CPP mission, both the collected images, and the processed results (to generate 2D, 3D, elevation, and plant health maps) are included.</p> <p>In this <a title="mUDAI - ChoosePath platform guide" href="https://sites.google.com/view/mudai-platform/" target="_blank" rel="noopener">page</a> you can find a guide for the on-line platform hosting demo instances of the algorithms used for the deployment of all experiments.</p> <p>In case you use this data, please cite the article:<br>(Article under review - more information to be included soon)</p>
Planning for the Future: How ICT Professors Approach Retirement and Post-Career Life
<p><strong>Context</strong>: Professors' attitudes toward retirement vary widely, ranging from enthusiasm to reluctance. Some see it as a new beginning, while others perceive it as an intensified continuation of their academic efforts. As an evolving process, professor retirement is becoming increasingly relevant due to the aging academic workforce in higher education institutions worldwide. <strong>Goal</strong>: This study aims to investigate the human, organizational, legal, and regulatory factors that may affect the retirement process of Information and Communication Technology (ICT) professors. <strong> Method</strong>: We conducted a survey with 176 ICT professors from various universities across the country to gather data on their perceptions and preparations for retirement. <strong>Results</strong>: Our findings reveal that most professors begin to consider retirement early (31.3%) or mid-career (21.6%). Over 64% plan to engage in leisure activities and travel after retirement, while continuing to contribute to academic research. Additionally, many reported building a financial reserve during their active professional life to supplement their income and maintain their standard of living. <strong>Conclusion</strong>: ICT professors are increasingly aware of the importance of retirement planning, with many adopting strategies to ensure a comfortable future and continue their academic contributions. Financial reserves and post-career activity planning are key aspects of this process. However, most universities do not offer training courses to prepare them for retirement.</p> <p><strong>Keywords</strong>: Retirement, ICT Professors, Post-Career Life</p>
Linked collectors and determiners for: Base de datos de especies y especímenes de la flora costera del norte de Chile (plan RECOGE).
Natural history specimen data linked to collectors and determiners held within, "Base de datos de especies y especímenes de la flora costera del norte de Chile (plan RECOGE)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/dc0ac5b3-f7f6-49ef-82ab-80b79eace946">https://bionomia.net/dataset/dc0ac5b3-f7f6-49ef-82ab-80b79eace946</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/dc0ac5b3-f7f6-49ef-82ab-80b79eace946">https://gbif.org/dataset/dc0ac5b3-f7f6-49ef-82ab-80b79eace946</a>. Formatted as a Frictionless Data package.
Tour planning validation dataset
<p>This is the dataset produced during the validation of the Viarota scenario for developing personalised city break tours. The mocked data collected were generated during the RADON validation activities, using the Viarota application in a controlled testing environment.</p>
Raw Laser Profilometer Data of Plan de Platanos, Big Piute and Water Man Hill Faults
<p>Raw laser profilometer data<strong> (Elevation matrices)</strong> for: </p> <ol> <li>Plan de los Platanos Fault (<strong>PP</strong>), Autlan de Navarro, Jalisco, Mexico---> Andesite (<strong>and</strong>)</li> <li>Big Piute Low Angle Normal Fault (<strong>BP</strong>), Mojave, Southern California--->quartzite (<strong>qtz</strong>)</li> <li>Waterman Hills Detachment Fault (<strong>WH</strong>), Mojave, Southern California---> mylonitized metasedimentary (<strong>mms</strong>)</li> </ol> <p><strong>Instrument:</strong> Taylor Hobson TALYSCAN 150 profilometer using a no-contact laser gauge</p> <p><strong>Vertical Resolution:</strong> 252nm</p> <p><strong>Traversing speed:</strong> 2mms-1</p> <p><strong>Horizontal Spacing:</strong> 5 µm (except for WH-03a1, which is 4 µm instead)</p> <p><strong>Unit of length</strong>: meters (m)</p> <p> </p> <p> </p>
Fig. 31. Floor plan for the 1927 in A History Of Herpetology At The American Museum Of Natural History
Fig. 31. Floor plan for the 1927 New Hall of Reptile and Amphibian Life. From an undated sketch in the departmental archives, showing location and subject matter of cases.
Text-fig. 4. Distribution of arsinoitheres in Africa. Reconstruction of Arsinoitherium is adapted from Pomerol (1973) (the body in the image is probably too similar to that of an elephant, but the reconstruction gives an idea of the dimensions and possible body plan of Arsinoitherium). in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia
Text-fig. 4. Distribution of arsinoitheres in Africa. Reconstruction of Arsinoitherium is adapted from Pomerol (1973) (the body in the image is probably too similar to that of an elephant, but the reconstruction gives an idea of the dimensions and possible body plan of Arsinoitherium).
Text-fig. 6. Comparative analysis of the three genera (Prynadaeopteris RADCZ., Geperapteris S.V.MEYEN, Tumidopteris NAUGOLNYKH) with possible affinity to Gleicheniaceae from the Permian deposits of Angaraland. a–c: Tumidopteris NAUGOLNYKH; d–f: Prynadaeopteris RADCZ. (based on Radczenko 1955, 1956, Naugolnykh 2013); g–i: Geperapteris S.V.MEYEN (based on Meyen 1982, Naugolnykh 2013). Left column – sori in plan (frontal view); central column – leaves; right column – sori in cross-section; FL – flattened sori; HS – hemispherical sori. Scale 1 cm (b, h), 5 mm (e), 1 mm (a, d), 0.5 mm (g); c, f, i – figures schematically drawn without scale. in A New Species Of The Genus Tumidopteris Naugolnykh From The Permian Of The Pechora Cis-Urals, Russia
Text-fig. 6. Comparative analysis of the three genera (Prynadaeopteris RADCZ., Geperapteris S.V.MEYEN, Tumidopteris NAUGOLNYKH) with possible affinity to Gleicheniaceae from the Permian deposits of Angaraland. a–c: Tumidopteris NAUGOLNYKH; d–f: Prynadaeopteris RADCZ. (based on Radczenko 1955, 1956, Naugolnykh 2013); g–i: Geperapteris S.V.MEYEN (based on Meyen 1982, Naugolnykh 2013). Left column – sori in plan (frontal view); central column – leaves; right column – sori in cross-section; FL – flattened sori; HS – hemispherical sori. Scale 1 cm (b, h), 5 mm (e), 1 mm (a, d), 0.5 mm (g); c, f, i – figures schematically drawn without scale.
Fig. 1. Circuit plan for bio-amplifier. For stages A - H in Scientific note A portable bio-amplifier for electric fish research: design and construction
Fig. 1. Circuit plan for bio-amplifier. For stages A - H refer to text. Boxes with dashed lines refer to components external to circuit board. Symbols follow universal engineering protocol.
fig. 1, Plantilla venedictoƒƒae n. sp., holotype 5, habitus (G.><2,4). Fig. 2 et 3, Pl. venedictoƒƒae n. sp .. holotype 5* 2, gros plan sur l'aire striée mésonotale. le cymbacalypte métathoracique et Porgane cymbalique du côté gauche (G. x 11) 3. vue mettant en évidence les sternites membranisés (G. x 8.7). in Description d'une troisième espèce du genre Plautilla Stal [Hom. Cicadoidea Plautillidae]
fig. 1, Plantilla venedictoƒƒae n. sp., holotype 5, habitus (G.><2,4). Fig. 2 et 3, Pl. venedictoƒƒae n. sp .. holotype 5* 2, gros plan sur l'aire striée mésonotale. le cymbacalypte métathoracique et Porgane cymbalique du côté gauche (G. x 11) 3. vue mettant en évidence les sternites membranisés (G. x 8.7).
CAP1 - Planning and control of asphalt production - Planning algorithm implemented in R
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
CAP1 - Planning and control of asphalt production - Thermal balance data (.xlsx)
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
Sri Ksetra, Myanmar. Plan of Excavations at the Yahanda Mound (2014-16) HMA.59
<p>Sri Ksetra, Myanmar. Plan of Excavations at the Yahanda Mound (2014-16). HMA.59 <strong>Coordinates: </strong><a href="http://wikimapia.org/#lang=en&lat=18.783569&lon=95.287750&z=16&m=bs&show=/41900016/Yahanda-gate-excavation-site&search=Sri%20Ksetra">18°47'27"N 95°16'49"E</a> © Ministry of Culture, Myanmar.</p>
Habitat Assessment and Restoration Planning (HARP) Model for the Snohomish and Stillaguamish River Basins
<p>Model code (R) to accompany the 2023 NOAA report "Habitat Assessment and Restoration Planning (HARP) Model for the Snohomish and Stillaguamish River Basins"</p>
Supporting Information for the Journal Article "Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning"
<p>This data set contains all data produced when exploring the Williamson ether synthesis starting from iodoethane and phenol.</p> <p><br> The set is structures as follows:</p> <ul> <li>analysis: Contains the script used to analyze the exploration and the output of said script</li> <li>check_barrier: Contains the output of the manual calculations done to check the barrier of the reaction</li> <li>exploration: Contains the scripts used to initialize and carry out the exploration as well as the two starting structures as XYZ files</li> <li>raw_data: a dump of the MongoDB database with all the data produced during the exploration</li> </ul>
Case study for Sustainable agri-food supply chain planning through multi-objective optimisation
<p>Case study used to solve the multi-objective model proposed in: Sustainable agri-food supply chain planning through multi-objective optimisation</p>
Sustainable Mobility Plan at Area Science Park
<p>This dataset contains data related to the Sustainable Mobility Plan (in Italian: PSCL, Piano Spostamenti Casa Lavoro) of Area Science Park (<a href="http://www.areasciencepark.it">www.areasciencepark.it</a>). It includes information on the number of public transport users, the number of trips made, the percentage of trips made by public transport, the average distance travelled, the number of parking spaces available and the occupancy rate of parking spaces. The purpose of the dataset is to provide open access to the information on which planning is based, and to enable the assessment of the implementation of the plan and its impact on mobility.</p> <p>The main objective of the Sustainable Mobility Plan is to describe the current situation and the initiatives launched and planned for the structural and permanent reduction of the environmental impact resulting from systematic home-work mobility. The measures envisaged in the plan concern all the five axes of action suggested by the guidelines and aim at the promotion of agile work, the study of feasibility studies for initiatives in favor of public transport, carpooling, cycle mobility, as well as favoring the use of electric vehicles.</p> <p>Files available:</p> <ul> <li>Conference Paper: “A roadmap to sustainable mobility: harvesting the low-hanging fruits while investing to achieve net zero emissions” presented at IASP 2023 Conference (29/09/2022)</li> <li>PSCL 2021 (document and dashboard)</li> <li>Survey</li> <li>Map</li> </ul>
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.