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2,373 results for “working”
Gender segregation at work over the business cycle: evidence from selected EU countries
<p>In this paper, we investigate whether gender employment rate responses to upward and downward fluctuations in the business cycle are symmetric and whether these responses differ depending on gender employment segregation in sectors and on different type of welfare states using the Esping-Andersen’s classification. We use the VAR model both in linear and non-linear (asymmetric) specification of GDP shocks and impulse response function. We find no convincing evidence of discouraged worker effect as it occurs in neither country fully,<strong> </strong>which suggests not hidden unemployment but rather the phenomenon of involuntary part-time workers becoming more common with the increase of precarious employment. Furthermore, we find that the pattern of gender employment adjustments to GDP fluctuations indicates that the gender sectoral segregation is a deeply entrenched feature within given economic sectors (construction, education, and accommodation) in all studied countries (Germany, Poland, and Portugal). Hence, this stagnation of gender segregation contributes to the preservation of gender pay differentials in spite of many years of equal pay legislation in the EU members.</p>
Selected boulders at Copernicus central peak from L. Sun and P. Lucey Lunar boulder study work
<p>This file contains the location of 54 boulders and soils on Copernicus central peak that were studied by Sun L. and Lucey P. G. in their lunar boulder study paper. This file can be uploaded to the LROC Quickmap website (https://quickmap.lroc.asu.edu/) for checking the location of each boulder and their nearby soils.</p>
Gait behavioral and neuromuscular characterization in response to increasing working memory load while walking under optic flow perturbations in young adults
<p>The precise role of cognitive control on optic flow processing during locomotion has rarely been investigated. Therefore, this study aimed to determine whether coping with unreliable visual inputs during walking requires attentional resources. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudo-random oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). In all conditions, subjective mental workload was estimated (raw NASA-TLX). For kinematic variables, mean, standard deviation, statistical persistence and step-to-step error correction were computed from gait time series in mediolateral and anteroposterior directions. For EMG variables of soleus and gluteus medius, the full width at half maximum and the variance ratio were calculated. Performance on N-back tasks was assessed using mean reaction time and d-prime. Cognitive performance was not affected by simultaneous walking in any optic flow condition. Gait variability was altered under POF compared to NOF, so that young adults sought to counteract those perturbations by adopting an effortful gait control strategy, independently of concurrent working memory (WM) load. Increasing WM load led changes first at the neuromuscular level, then at the behavioral level, with a prioritization of gait control in the mediolateral direction. Interestingly, dual-tasking lowered the effects of POF but in the anteroposterior direction only. These findings and their theoretical implications provide valuable insight into the complex interaction effects of cognitive and visual constraints on gait control during treadmill walking.</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Numerical analysis of working paper "Joint integrated production-maintenance policy of a deteriorating equipment considering random yield and maintenance delay"
<p>This is the numerical analysis code using in the working paper "Joint integrated production-maintenance policy of a</p> <p>deteriorating equipment considering random yield and maintenance delay"</p>
Databases and information systems for research output: digital humanities outlook (DARIAH Bibliographical Data Working Group, September, 30th 2022)
<p>The video recording of the workshop "Databases and information systems for research output: digital humanities outlook" organized by DARIAH Bibliographical Data Working Group.</p>
COVID-19: data and indicators to measure the return to work in Area Science Park after the emergency phase
<p>Data to measure the impact of SARS-CoV-2 virus on work, activities and services of Area Science Park and the return to work after the emergency epidemiological phase.</p> <p>Files available:</p> <ul> <li>Report Restart Area</li> <li>Stima_Presenze_Area_Science_Park</li> </ul> <p> </p> <p> </p>
IF 6/S4/2016 – WORK IN PROGRESS
[Roman Rural Landscapes at the Department of Classical Archaeology, University of Vienna](http://rrl.univie.ac.at/) # Single layer documentation | 3D model derived from SFM/MVS algroithms # 3D model of trench 4 at the roman rural site at [Molino San Vincenzo (Tuscany/ITA)](http://bit.ly/msv-gm) at the end of the 2016's campaign (*Interface 6/S4/2016*) # more info at http://bit.ly/excavationMSV # model by [Dominik Hagmann](http://homepage.univie.ac.at/dominik.hagmann) and [Andreas Steininger](https://sketchfab.com/a.steininger) (both University of Vienna) 2016 # The model is orientated North. Source: Objaverse 1.0 / Sketchfab
Кепка рабочая • Working Cap
1949–1953 22 x 25 x 5 cm Найдена на месте одного из лагерей, где заключенные занимались строительством железной дороги "Салехард–Игарка" (т.н. Мертвая Дорога), ветки, идущей со стороны Игарки, в районе поселка Ермаково. Экспедиция была организована Музеем вечной мерзлоты (Игарка) до 2002 г. The cap was found at the territory of one of the former labor camps, were the inmates were involved into the construction of the Salekhard – Igarka railroad (so called Dead Road), the rail line incoming from Igarka, near Ermakovo village. The expedition was organised by the Igarka Museum of Permafrost before 2002. Source: Objaverse 1.0 / Sketchfab
US 11300/S2/2015 (A2) – WORK IN PROGRESS
[Department of Classical Archaeology, University of Vienna](http://klass-archaeologie.univie.ac.at/) # Single layer documentation | 3D model derived from SFM/MVS algroithms # 3D model of the filling (layer *US 11300/S2/2015*) of a [kiln (layer *US USS 12100/13300/13600/S2/2015*)](http://https://skfb.ly/Pnox) in [*trench 2/2015*](http://https://skfb.ly/Pno9) of the roman rural site at [Molino San Vincenzo (Tuscany/ITA)](http://goo.gl/u1lNEU): second stage of excavation. The stratigraphic unit is moderately removed (for the other stages see: [*US 11300/S2/2015 (A1)*](http://https://skfb.ly/PnoR) and [*US 11300/S2/2015 (A3*](https://skfb.ly/PnoY)). The model shows the parts of the trench which were covered by the images taken for the IBM workflow. # more info at [rrl.univie.ac.at](https://rrl.univie.ac.at/forschung/msv/) # model by Johannes Reiter and [Dominik Hagmann](http://univie.academia.edu/DominikHagmann) (both University of Vienna) 2015 # The model is orientated North roughly. Source: Objaverse 1.0 / Sketchfab
US 9900/S2/2015 – WORK IN PROGRESS
[Department of Classical Archaeology, University of Vienna](http://klass-archaeologie.univie.ac.at/) # Single layer documentation | 3D model derived from SFM/MVS algroithms # 3D model of the foundation (layer *US 9900/S2/2015*) and its surroundings in trench 2 of the Roman rural site at Molino San Vincenzo (Tuscany/ITA). The model shows the parts of the trench which were covered by the images taken for the IBM workflow. # more info at [rrl.univie.ac.at](http://rrl.univie.ac.at/forschung/msv/) # Find the site at[ Google Maps](https://goo.gl/u1lNEU) # model by Johannes Reiter and [Dominik Hagmann](http://univie.academia.edu/DominikHagmann) (both University of Vienna) 2015 # The model is orientated North. Source: Objaverse 1.0 / Sketchfab
Old works: Treasure Chest
Just some old works. Render: https://www.instagram.com/p/B4VgTcro9wr/ Source: Objaverse 1.0 / Sketchfab
COAR RDM (Research Data Management) Working Group
<p>This presentation was made in the internal meeting of COAR RDM (Research Data Management) Working Group. It covered building up support structure for RDM and the requirements for infrastructures including a use case from Vienna University.</p>
Hofstede's Cultural A Case Study: Working Performance of Chinese Expatriates in Dubai
<p>The data are from the paper " Hofstede’s Cultural A Case Study: Working Performance of Chinese Expatriates in Dubai".</p>
Working at home from L3 Scientific Applications Consulting LLC
<p>Photograph of home office space then passed through an 'edges' filter, shows three computer screens (one with MacOS logo, one with Fortran code, one with plot of Lake Michigan), a Blue Snowball, a keyboard, and some other peripherals.</p>
Poor work ability is associated with workplace violence in nurses. A longitudinal study.
<p>Dataset of an article to be published on IJERPH</p>
HEartS Professional Survey: Charting the effects of COVID-19 on working patterns, income, and well-being among arts professionals in China (October 2020, August 2021)
<p>These data were collected using the HEartS Professional China survey from performing arts workers in China in October 2020 and August 2021. HEartS Professional China is an adaptation of the HEartS Professional surveys which were used in 2020-2021. All the surveys were designed as multi-strategy data collection tools with two main purposes: (1) to chart working patterns, income, sources of support, and indicators of mental and social well-being to identify trends in the effects of the lockdown at the time and (2) to explore the individual work and wellbeing experiences of performing arts professionals in their own words, to identify the subjective effects of lockdown in terms of challenges and opportunities. The survey covers six areas: 1) demographics; (2) information on illness or self-isolation related to COVID-19; (3) work profiles and income; (4) changes to work profiles and income as a result of the pandemic, as well as sources of support; (5) open-response questions about work and wellbeing experiences of lockdown including challenges and opportunities; and (6) validated measures of health, wellbeing, and social connectedness. The HEartS Professional surveys are adaptations of the HEartS Survey which charts the Health, Economic, and Social impacts of the ARTs (<a href="https://doi.org/10.5061/dryad.3r2280gdj">https://doi.org/10.5061/dryad.3r2280gdj</a>).</p>
Segregation of losses, efficiency and temperature register of an induction generator working at overrated variable load conditions
Open the record for dataset details and reuse information.
Citation network dataset covering the work of RP Millar and its citing literature
<p>The following describes the citation network datasets that underpins the manuscript “A career in numbers: a citation network analysis of the work of RP Millar and his contribution to GnRH research” [1].</p> <p><strong>Data collection</strong></p> <p>We retrieved data from the Web of Science Core Collection under the University of Edinburgh’s subscription in January 2024. We sought to retrieve all indexed papers of Professor Robert P. Millar (RPM). We searched the following <em>AU = (Millar, R)</em>, and then retrieved records that corresponded to his WoS profile (n=428 records) and an additional 49 paper that were authored by Robert but had not been included in his WoS record – validating the records against a CV of his published works.</p> <p>We retrieved the full citation history as record by Web of Science to these papers from other indexed records. The 477 RPM papers had been cited 21,677 times by 11,138 documents by date of retrieval, and removing self-citations left 19,256 citations by 10,719 documents. We then retrieved all metadata from WoS concerning the 477 RPM papers and the 10,719 citation papers, resulting in a dataset covering 11,196 documents.</p> <p><strong>Citation network dataset</strong></p> <p>We constructed a citation network dataset by parsing data from each paper’s full bibliography consisting of:</p> <p>i. ‘Edge-list’ that records citation links from a citing to a cited document. This is constructed by assigning unique IDs to each retrieved paper and to every unique reference string contained in their bibliographies. The edge list is composed of a ‘Source’ column that contains the ID of the <em>citing</em> document and a ‘Target’ column containing the IDs of its citations, with one record per row. Given that we were only interested in citations between the WoS retrieved documents, we discarded any reference string that represented a document outwith our search.</p> <p>ii. ‘Node-attribute list’ that contains the ID, with relevant metadata contained in adjacent columns to identify documents, including authors, title of publication, journal, year of publication. We also parsed into this dataset the WoS full citation count for each paper and the total number of references in the bibliographies of each paper.</p> <p>This results in a dataset containing 11,196 nodes and 115,834 edges between nodes. We removed a total of 67 papers for which metadata was incomplete and/or corrupted. We further focussed on the largest interconnected component, removing nodes with no connections (isolates) or smaller components that were detached from the main network. We excluded papers <10 references to remove meeting abstracts and other minor journal items, and papers not published in English. This resulted in a final dataset containing 10,901 nodes and 113,742 edges, and it is this dataset that we share as it is the basis for the analyses within the paper.</p> <p><strong>Description of dataset variables</strong></p> <p><strong>‘RPM_Edgelist.csv’</strong> is a comma-separate values file that consists of all 113,742 citations between the 10,901 documents of the citation network analysed in the manuscript. The columns refer to:</p> <ul> <li>‘<em>Source</em>’, the unique identifier for the <em>citing </em>document</li> <li>‘<em>Target’</em>, the unique identifier for the <em>cited</em> document</li> <li>‘<em>Syr</em>’, the year of publication of the <em>citing</em> document</li> <li>‘<em>Tyr</em>’, the year of publication of the <em>cited</em> document</li> <li>‘<em>SC</em>’, the cluster ID of the <em>citing</em> document</li> <li>‘<em>TC</em>’, the cluster ID of the <em>cited </em>document</li> </ul> <p><strong>‘RPM_Nodelist.csv’</strong> is a comma-separate values file that consists of the 10,901 documents of the citation network analysed in the manuscript. The columns refer to:</p> <ul> <li>‘<em>Id</em>’, the unique ID assigned to a document that corresponds with the edgelist</li> <li>‘<em>Reference string</em>’, the reference string of the document</li> <li>‘<em>WoS ID</em>’, the unique accession number assigned to a document by the Web of Science. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.</li> <li>‘<em>Authors</em>’, all authors formatted by full last name and initials</li> <li>‘<em># of authors’</em>, number of authors</li> <li>‘<em>Title</em>’, title of document</li> <li>‘<em>Publication year</em>’, publication year of document</li> <li>‘<em>Document type</em>’, document type defined by WoS (e.g. article, review, etc.)</li> <li>‘<em>Total references</em>’, total number of references within a documents bibliography as recorded by WoS</li> <li>‘<em>Total WoS citations</em>’, total number of citations recorded to a document from other documents indexed in the Web of Science</li> <li>‘<em>Indegree</em>’, total number of within network citations (i.e. counting only citations from other papers retrieved by our query)</li> <li>‘<em>Outdegree</em>’, total number of within network references (i.e. counting only reference to other papers retrieved by our query)</li> <li>‘<em>Degree</em>’, total number of node connections (i.e. indegree + outdegree)</li> <li>‘<em>Class</em>’, variable used to distinguish between RPM’s publications (‘RPM’) and the citing documents (‘CITE’)</li> <li>‘<em>Cluster</em>’, provides the cluster membership number as discussed within the manuscript. This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.67 | 25 clusters).</li> </ul> <p><strong>References</strong></p> <p>[1] Leng, R. I., Leng. G. (Under review). A career in numbers: a citation network analysis of the work of RP Millar and his contribution to GnRH research. <em>J. Neuroendocrinol</em></p> <p>All bibliographic data included in this study are derived originally from Clarivate™ (Web of Science™) and downloaded in January 2024. © Clarivate 2024. All rights reserved. </p>
Dataset for "Distinguishing between temporary and permanent removal in verbal working memory"
<p><strong>Dataset for “Distinguishing between temporary and permanent removal in verbal working memory”</strong></p> <p><strong>Manuscript's abstract</strong></p> <p>We employed the retro-cue paradigm to examine performance improvements resulting from permanent versus temporary removal in verbal working memory. Permanent removal entails discarding a subset of WM representations marked as definitively irrelevant, while temporary removal involves momentarily setting aside the uncued subset of representations from the attentional focus, preserving accessibility for later refocusing. We observed that permanent and temporary removal led to marked progressive reductions in reaction time and errors across cue-target intervals (200, 400, 800, and 1600 ms), reflecting the gradual simplification of the search set following informative cues. Although removal conditions did not differ in accuracy, responses were slower in the temporary removal condition, especially at the longest interval. A key finding was that performance in the temporary removal condition, but not in the permanent removal condition, was modulated by the presentation order of the target's memory set. This order effect was also observed in a non-removal control condition where double retro-cues marked all presented information as relevant. We suggest that these order effects depend on maintaining the integrity of the retrieval structure (all the contextual cues) needed to guide attentional access to specific representations, including those provisionally set aside in the temporary removal condition. In conclusion, the primary distinction between permanent and temporary removal processes is that permanent removal simplifies the retrieval structure by eliminating unnecessary contextual cues, resulting in a greater reduction in the complexity of the search set compared to temporary removal.</p> <p> </p> <p><strong>Dataset description</strong></p> <p>FILES: one_row_per_trial.csv, one_row_per_trial.xlsx</p> <p>Column 1: participant_id (from 1 to 56).<br>Column 2: participant_sex (0 = female; 1 = male; 2 = other).<br>Column 3: participant_age (in years).<br>Column 4: frame_1_locus (the location of the presentation frame where the first set is presented; up: above the fixation cross; down: below the fixation cross).<br>Column 5: trial_number (from 17 to 64, block 1; from 81 to 128, block 2; from 145 to 192, block 3; from 209 to 256, block 4. The 16 missing trials preceding each block were practice trials).<br>Column 6: condition (c1 = permanent removal; c2 = temporary removal; c3 = control-2; c4 = control-4)<br>Column 7: cue_target_interval (i1 = 200 ms; i2 = 400 ms; i3 = 800 ms; i4 = 1600 ms).<br>Column 7: presentation_frame (f1 = presentation frame 1 = the target belonged to the set presented first; f2 = presentation frame 2 = the target belonged to the set presented second).<br>Column 8: rt1 (reaction time; 0 = no response within the 1200-ms response window) <br>Column 9: acc1 (accuracy; 1 = correct response; 0 = incorrect response or no response within the response window)<br>Column 10: rt2 (reaction time in the second response of the temporary removal condition; reaction time; 0 = no response within the 1200-ms response window).<br>Column 11: acc2 (accuracy in the second response of the temporary removal condition; 1 = correct response; 0 = incorrect response or no response within the response window).</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.