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1,199 results for “aspect”
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 9: Aspects of ENCI V.: Contesting the current system
<p>This document is Part 9 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</p>
Icons illustrating aspects of data organization from the Data Literacy Initiative (DaLI) at TH Köln
<p>Icons created as part of the research project Data Literacy Initiative (DaLI) at TH Köln - University of Applied Sciences in Cologne, Germany. They illustrate aspects of best practices for data organization as recommended in Karl W. Broman & Kara H. Woo (2018) Data Organization in Spreadsheets, The American Statistician, 72:1, 2-10, DOI: 10.1080/00031305.2017.1375989.<br> </p> <p>When using any of the images please include the following attribution together with the DOI: This image was created by Jule Marie Schacht and Juliane Piecha for the Data Literacy Initiative (DaLI) at TH Köln and is used under a CC-BY license.</p> <p>The Data Literacy Initiative (DaLI) at TH Köln develops an interdisciplinary, modular program offering data literacy training to students from all fields.</p> <p>More information on the Data Literacy Initiative (DaLI) (in German): https://www.th-koeln.de/dali</p> <p>Illustrations created by: Jule Marie Schacht</p>
Topography Aspect - Ipswich Watershed - Idrisi Raster File
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This is the aspect model for the study area. The source elevation tile data was provided on the MassGIS website www.state.ma.us/mgis/massgis.htm in ESRI-format shapefile format and imported into IDRISI software using the ShapeIdr command. The resulting vector elevation files were converted to raster format using successive Lineras macro commands. This has the effect of mosaicing the tiles as well. The raster image was filtered once using a low-pass (mean) filter, then masked to the Ipswich study area parameters (extent). The aspect map was created using the surface analysis module. This datalayer was produced as part of a research project concerning the Ipswich River Watershed.
Legal Aspects of Research Data
<p>A video introduction to the legal aspects of research data management. </p> <p>Find out more about research data and open data training from: <a href="https://discipline-workshops.com/">https://discipline-workshops.com/</a></p>
FICI'RE 51.—Female genitalia, ventral aspect, with spermathecal reservoir, of Xystosomus parainsularis, Nueva Grenada (Colombia). in Studies of the subtribe Tachyina (Coleoptera: Carabidae: Bembidiini), Part I: A revision of the Neotropical genus Xystosomus Schaum
FICI'RE 51.—Female genitalia, ventral aspect, with spermathecal reservoir, of Xystosomus parainsularis, Nueva Grenada (Colombia).
Figure 3 in Aspects of natural history in a sand boa, Eryx muelleri (Erycidae) from arid savannahs in Burkina Faso, Togo, and Nigeria (West Africa)
Figure 3. Relationships between (a) Snout-Vent-Length (SLV) and Tail Length (TL), and between (b) SVL and Head Length (HL) in Eryx muelleri. Specimens from Togo, Burkina Faso and Nigeria were pooled.
Figure 1 in Aspects of natural history in a sand boa, Eryx muelleri (Erycidae) from arid savannahs in Burkina Faso, Togo, and Nigeria (West Africa)
Figure 1. (a) Eryx muelleri from Kebbe, north-western Nigeria (Photo: Luca Luiselli); (b) dry savannah habitat of Eryx muelleri in northern Burkina Faso (Photo: Emmanuel Hema).
Figure 3 in Harpacticoida (Copepoda) of the northern East Sea (the Sea of Japan) and the southern Sea of Okhotsk: diversity, taxocenes, and biogeographical aspects
Figure 3. Biogeographical structure of the faunas from the three main species complexes in the East Sea and the southern part of the Sea of Okhotsk. Relative species abundances with different distribution ranges are presented.
Figs 1-5. Anasillomos chrysopos spec. nov. paratype male. 1. Antenna, lateral aspect. 2 in The Genus Daspletis Loew, 1858 And The Description Of Two New Genera, Anasillomos And Remotomyia (Diptera: Asilidae: Stenopogoninae)
Figs 1-5. Anasillomos chrysopos spec. nov. paratype male. 1. Antenna, lateral aspect. 2. Head and part of thorax. 3- 5. Genitalia. 3. Lateral. 4. Ventral. 5. Dorsal.
Figs 11 15. Daspletis hirtus Ricardo. 11-12. Kalahari Gemsbok Park male. 11. Head. 12. Antenna, lateral aspect. 13- 15. Sawmills topotype male genitalia. 13. Lateral. 14. Ventral. x4 in The Genus Daspletis Loew, 1858 And The Description Of Two New Genera, Anasillomos And Remotomyia (Diptera: Asilidae: Stenopogoninae)
Figs 11 15. Daspletis hirtus Ricardo. 11-12. Kalahari Gemsbok Park male. 11. Head. 12. Antenna, lateral aspect. 13- 15. Sawmills topotype male genitalia. 13. Lateral. 14. Ventral. x4. Dorsal.
Aspect J
<p>Reference</p> <p>Studies who have been using the data (in any form) are required to include the following reference:</p> <p>@INPROCEEDINGS{7372035, author={Lam, An Ngoc and Nguyen, Anh Tuan and Nguyen, Hoan Anh and Nguyen, Tien N.}, booktitle={Automated Software Engineering (ASE), 2015 30th IEEE/ACM International Conference on}, title={Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports (N)}, year={2015}, pages={476-481}, keywords={Bridges;Computer bugs;Feature extraction;History;Information retrieval;Metadata;Software;Bug Localization;Bug Reports;Deep Learning;Deep Neural Network;Information Retrieval}, doi={10.1109/ASE.2015.73}, month={Nov},}</p> <p>About the Data</p> <p>This dataset is one of the Datasets donated by An Ngoc Lam.</p> <p>Overview of Data</p> <p>The data is present in 2 files:</p> <p>“AspectJ.xlsx” : A spreadsheet with the bug-ids, commits, its summary, files etc.</p> <p>“AspectJ.xml” : An xml file with more detailed information than the above spreadsheet(detailed files changed).</p> <p>Attribute Information</p> <p>The spreadsheet contains a table with the “bug_id”, “summary”, “description”, “time_reported”, “commit associated”, “status of commit” and “files committed”.</p> <p>The xml contains the above information and additionally the lines associated with the commit.</p> <p>Paper Abstract</p> <p>Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.</p>
Supplementary material to: Russian verbal aspect and the activation of event knowledge: Processing typical and atypical location adverbials in perfective and imperfective sentences
<p>Supplementary material for a self-paced reading experiment:</p> <ol> <li>Material: contains the verbal stimuli (experimental and filler sentences)</li> <li>raw_data.zip: E-Prime output for all 50 participants (tab-separated txt-files)</li> <li>spr_aspect_respinf.txt: Basic information on the participants; tab-separated txt-file</li> <li>spr_aspect_preprocessing_subm2_fin.R: data preprocessing and calculation of correct responses per speaker in R; R script<br>Input: <br>- Files from the "raw_data"-folder<br>- spr_aspect_respinf.txt<br>Output: <br>- spr_aspect_respinf_corr_resp.txt: same as spr_aspect_respinf.txt + number of correct responses per participant; tab-separated txt-file<br>- spr_aspect_all.txt: relevant data from all participants in one file; tab-separated txt-file<br>- spr_aspect_all_without-outlier.txt: same as spr_aspect_all.txt, but outliers set to NA; tab-separated txt-file</li> <li>spr_aspect_figures-stats_subm2_fin.R: plots figures and calculates statistics (descriptive statistics and GLMM) in R, R script<br>Input: <br>- spr_aspect_all_without-outlier.txt<br>Output:<br>- spr_aspect_avg.txt: Mean RT and SD per condition; tab-separated txt-file<br>- Figures</li> </ol>
A collection of AI generated images visualising various RDM aspects
<p>This publication contains images visualising various RDM aspects. These images were generated by the <a href="https://www.forschungsdaten.uni-bonn.de/en" target="_blank" rel="noopener">Research Data Service Center</a> team at the University of Bonn and are used in the workshop "Research Data Management: A Crash Course" conducted since 2021 by the Research Data Service Center. The slide deck is available as a related publication (see the related works section below for details).</p> <p>The images were generated with the help of <a href="https://help.openai.com/en/articles/8932459-creating-images-in-chatgpt">ChatGPT</a>. </p> <p>In this version, due to legal reasons, we changed the images.</p>
Fig. (10-17): (10) Dichrogaster aestivalis, fore wing; (11) C. armator, areolet of fore wing; (12) Mesostenus sp., areolet of fore wing; (13) Venturia canescens, ovipositor; (14) Barichneumon sp.; ventral aspect of metasoma; (15) Ctenichneumon sp., ventral aspect of metasoma; (16) Exochus castaniventris, frontal view of head; (17) Diplazon laetatorius, frontal view of head. in Ichneumonidae from the Suez Canal region Egypt (Hymenoptera, Ichneumonoidea)
Fig. (10-17): (10) Dichrogaster aestivalis, fore wing; (11) C. armator, areolet of fore wing; (12) Mesostenus sp., areolet of fore wing; (13) Venturia canescens, ovipositor; (14) Barichneumon sp.; ventral aspect of metasoma; (15) Ctenichneumon sp., ventral aspect of metasoma; (16) Exochus castaniventris, frontal view of head; (17) Diplazon laetatorius, frontal view of head.
Fig. (18-26): (18) D. laetatorius, hind wing; (19) Netelia sp., hind wing; (20) Netelia sp., frontal view of head; (21) D. laetatorius, propodeum; (22) Syrphophilus bizonarius, propodeum; (23) D. laetatorius, dorsal aspect of metasoma; (24) Netelia sp., lateral aspect of first metasomal segment showing glymma; (25) Exetastes syriacus, ovipositor; (26) Exeristes roborator, ovipositor. in Ichneumonidae from the Suez Canal region Egypt (Hymenoptera, Ichneumonoidea)
Fig. (18-26): (18) D. laetatorius, hind wing; (19) Netelia sp., hind wing; (20) Netelia sp., frontal view of head; (21) D. laetatorius, propodeum; (22) Syrphophilus bizonarius, propodeum; (23) D. laetatorius, dorsal aspect of metasoma; (24) Netelia sp., lateral aspect of first metasomal segment showing glymma; (25) Exetastes syriacus, ovipositor; (26) Exeristes roborator, ovipositor.
Importance rating of various operational aspects related to repair service processes
<h3>Contextual information</h3><p>The survey considers the scenario that repairs are conducted at a repair service centre but not as a repair service at home as, e.g., for washing machines. This allowed to split the sample into four groups, each of it referring to a different portable product to be repaired (bicycle, smartphone, vacuum cleaner and a generic product) in order to examine the generalisability of the results.</p><p>The survey is based on items related to repair operations and provides data concerning the importance of the different items. It considers the scenario that repairs are conducted at a repair service centre but not as a repair service at home as, e.g., for washing machines. The operational aspects concern three different stages of the repair process that can be influenced by the repair company: the prepurchase, service encounter, and post-encounter stage. Furthermore, socio-demographic data is included.</p><h3>Description of the data and file structure</h3><p>The dataset contains 107 aspects identified for potential improvement of repair operations. The importance of operational aspects was rated by survey participants on a 7-point Likert scale (extremely important to not at all important). Three additional questions for quality control (e.g., 'Please tick 1 = extremely important') tested the attention of the participants. </p><p>File 'Translation_of_items.ods' contains the items in German (original version) and English (translated). File 'Description_of_Data.ods' contains a description of the variables.</p><ul><li>We removed all answers with completion time of less than five minutes, as the pre-test showed that this was the minimum time for a focused completion of the survey.</li><li>All data rows with wrongly answered control questions were removed.</li></ul>
Research Data Management Aspects - A Mindmap
<p>Just my personal mind-map of research data management aspects. No guarantee to be complete, feel free to use it and give me feedback.</p>
Infografies de sis aspectes clau en el desenvolupament de projectes de ciència ciutadana
<p>Aquestes sis infografies descriuen sis aspectes clau en el desenvolupament de projectes de ciència ciutadana. Les sis idees clau són: cocreació, comunitats, eines i mètodes, dades, ètica i inclusió i acció. Cada infografia condensa diverses idees i missatges en tres columnes: per què és important l'aspecte, com es pot desenvolupar aquest aspecte i finalment un conjunt de recomanacions pràctiques.</p> <p>El material digital també està disponible al <a href="https://web.ub.edu/en/web/ciencia-ciutadana/">web</a> de la Universitat de Barcelona sobre ciència ciutadana. El material s'està utilitzant en activitats de formació certificades sobre ciència ciutadana i desenvolupat pel grup de recerca <a href="http://www.ub.edu/opensystems">OpenSystems</a>. El primer ús d'aquest material s'està fent durant un programa de formació certificada que ofereix IDP-ICE UB: Formació en ciència ciutadana: Introducció i aprofundiment en el desenvolupament de projectes de recerca participativa amb compromís social. Va tenir una durada de 10 hores i va estar orientat a un públic ampli que inclou majoritàriament personal d'universitats, representants d'organitzacions de la societat civil, estudiants universitaris i personal de l'administració pública.</p> <p>Infografia creada en el marc del projecte CSNOW amb el Grup de Treball de Ciència Ciutadana de l'IDP-ICE UB, coordinat per Josep Perelló amb l'assistència de Núria Coll-Bonfill. Les traduccions del català a l'anglès han estat possibles amb el suport del projecte TORCH EU. Disseny: <a href="https://minimalheroes.tv">Minimal Heroes</a>. </p>
Deliverable 2.3: Biodiversity impact estimates and documentation for indicators on multi-dimensional biodiversity aspects ready for use in WP3
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km2 resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a improve in previous deliverable 2.2.</p>
Disentangling developmental effects of play aspects in rat rough-and-tumble play
<p>Animal play encompasses a variety of aspects, with kinematic and social aspects being particularly prevalent in mammalian play behaviour. While the developmental effects of play have been increasingly documented in recent decades, understanding the specific contributions of different play aspects remains crucial to understand the function and evolutionary benefit of animal play. In our study, developing male rats were exposed to rough-and-tumble (RT) play selectively reduced in either the kinematic or the social aspect. We then assessed the developmental effects of reduced play on their appraisal of standardised human-rat play ('tickling') by examining their emission of 50-kHz ultrasonic vocalisations (USVs). Using a deep learning framework, we efficiently classified five subtypes of these USV across six behaviour states. Our results revealed that rats lacking the kinematic aspect in play emitted fewer USVs during tactile contacts by human and generally produced fewer USVs of positive valence compared to control rats. Rats lacking the social aspect did not differ from the control and the kinematically reduced group. These results indicate aspects of play have different developmental effects, underscoring the need for researchers to further disentangle how each aspect affects animals.</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.