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Dataset to Why Hydrogen Dissociation Catalysts do not Work for Hydrogenation of Magnesium by Selim Kazaz et al.,, Fig. 1, 2, 3, 4 , 5 ,6
<p>Datasets to publication S. Kazaz et al, Adv. Sci. 2023, 2304603c; Why Hydrogen Dissociation Catalysts do not Work for Hydrogenation of Magnesium; additional data/explanation upon request (corresponding authors)</p>
Quantitative results of the analysis of human native and bioengineered tissues corresponding to the work "Histological, histochemical and immunohistochemical characterization of NANOULCOR nanostructured fibrin-agarose human cornea substitutes generated by tissue engineering"
<p>Dataset containing the quantitative results of the histochemical and immunohistochemical analysis of the following human tissues:</p> <ul> <li>Control native cornea (CTR-C)</li> <li>Control native limbus (CTR-L)</li> <li>Artificial cornea generated by tissue engineering (HAC)</li> </ul> <p>Each tissue type was subjected to histochemical and immunohistochemical analyses and results were quantified using ImageJ software to determine average intensities and area fractions corresponding to positive staining signal for each marker.</p>
Ramoon's Work PATHWAYS T5.4
<p>The document refers to the database used in the Milestone 45 - Data on post-farm-gate impact collected for anticipatory LCA.</p>
Work-related burden of diseases and disorders
<p><span>The burden of the work-related diseases is a major global health challenge. This data provides the global, regional and country level estimates on the work-related burden of occupational diseases and accidents for the year 2019 in terms of deaths, disability adjusted life years (DALYs) and economic loss as a percentage of total gross domestic product (GDP). </span></p> <p><span>The data contains calculated estimates based on the employment figures, mortality rates, occupational injuries, accidents, self-reported occupational illnesses and injuries from electronic data sources of international organizations, institutions, and public websites. Risk ratios (RR) and population attributable fractions (PAF) for the risk factors outcome pairs were derived from literature. Estimated mortality and DALYs for a group of seven major diseases covering 120 risk-outcome pairs attributable to work are calculated at global, WHO regions and at country level. The details of the methodology of the data have been published already (<a href="https://www.sjweh.fi/article/4132"><span>https://www.sjweh.fi/article/4132</span></a>).</span></p> <p><span>In general, the method is based on the number of problems identified at work: injuries, illnesses, and disorders including fatal or no-fatal cases. This was implemented by calculating the deaths, Years of Life Lost (YLL), Years Lived with Disability (YLD) and their combination Disability Adjusted Life Years (DALY) based on primarily ILO numbers as adjusted by the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease and Injury (GBD) process outcomes. While there are existing estimates updated annually by the GBD process, latest for the year 2019, these do cover only a selected group of occupational risks. Therefore, the GBD 2019 outcomes would not be comparable to ILO Estimates and – if directly used – would end up in clear underestimates of the size of the problem. The differences between ILO and GBD2015 outcomes are reported elsewhere. </span></p> <div> <div> <p><span> </span></p> </div> </div>
Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.
<h1><a name="_Toc169648661"></a><span>Overview</span></h1> <p><strong><span> </span></strong></p> <p><span>The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “<em>WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students</em>”. <span> </span>Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.</span></p> <p><strong><em><span> </span></em></strong></p> <p><strong><em><span>Background and Related Work</span></em></strong></p> <p><em><span>As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].<span> </span></span></em></p> <p><em><span>“Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.<span> </span>The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces. </span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>2</span></em></strong><em><span><span> </span><strong>Goals of the Working Group</strong></span></em></p> <p><em><span>By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):</span></em></p> <p><em><span> </span></em></p> <p><em><span>RQ1: How does AI influence work engagement for IT professionals?</span></em></p> <p><em><span>RQ2: How does AI affect the socio-technical work dynamics for IT professionals?</span></em></p> <p><em><span>RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?</span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>3</span></em></strong><em><span><span> </span><strong>Methodology</strong></span></em></p> <p><em><span>This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.<span> </span>Further implications for computing curricula design and assessment will be developed from this analysis. </span></em></p> <p><span>REFERENCES</span></p> <p><span>[1]<span> </span>ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York.</span></p> <p><span>[2]<span> </span>Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? <em>Qualitative research in psychology</em>, <em>18</em> (3). 328-352.</span></p> <p><span>[3]<span> </span>Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science<span> </span>Competency Statements: A Process and Curriculum Model for the 21st Century in <em>Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education</em>, ACM, New York.</span></p> <p><span>[4]<span> </span>Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. <em>A Computing Curricula Series Report </em>ACM, New York.</span></p> <p><span>[5]<span> </span>Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in <em>2011 international symposium on empirical software engineering and measurement</em>, IEEE, 2011, 275-284.</span></p> <p><span>[6]<span> </span>Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in <em>2020 IEEE Frontiers in Education Conference (FIE)</em>, IEEE, Uppsala, Sweden. </span></p> <p><span>[7]<span> </span>Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. <em>Information and Software Technology</em>, <em>106.</em> 101-121</span></p> <p><span>[8]<span> </span>Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. <em>ACM Inroads</em>, <em>14</em> (2). 40-46.</span></p> <p><span>[9]<span> </span>Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. <em>arXiv preprint arXiv:2308.06921</em>.</span></p> <p><span>[10]<span> </span>Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. <em>arXiv preprint arXiv:2310.00658</em>.</span></p> <p><span>[11]<span> </span>Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in <em>Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5</em>, Springer, 158-172.</span></p> <p><span>[12]<span> </span>SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London.</span></p> <p><span>[13]<span> </span>Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. <em>Information and software technology</em>, <em>51</em> (1). 219-233.</span></p> <p><em><span> </span></em></p>
Metadata of interviewees in Work Packages 4&5 of the CLEVER project
<p>This Excel database lists metadata of interviewees that participated in the data collection of the CLEVER (Creating leverage to enhance biodiversity outcomes of global biomass trade) project in its Work Packages 4 and 5.</p>
Wireframes for the design and implementation of the National Edition of Aldo Moro's works website
<p>A series of low-fidelity graphical models, realized in Figma and used to drive the information architecture and content strategy of the website.</p>
Documentation diagrams of the National Edition of Aldo Moro's works
<p>A series of Graffoo and Draw.io diagrams that graphically represent the conceptual modeling of the Edition's data.</p>
IPCC Working Group 1 (WG1) Sixth Assessment Report (AR6) Annex III Extended Data
<p>Extended data relating to atmospheric abundences and effective radiative forcing from historical and future projections. Data is presented in abridged form in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) Working Group 1 (WG1) Annex 3. </p> <p>In this dataset, data is provided for all years, and includes additional scenarios not included in the published tables.</p> <p><strong>Contents</strong></p> <ul> <li>table A3.1: historical observed greenhouse gas (GHG) abundances. All subtables a-f in the printed report are combined into one CSV file.</li> <li>table A3.2: future projections (2020-2500) of GHG abundances for nine SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0-lowNTCF, SSP4-3.4, SSP4-6.0, SSP5-3.4-over, SSP5-8.5). Orignal data is from Meinshausen et al. (2020): https://doi.org/10.5194/gmd-2019-222 </li> <li>table A3.3: historical effective radiative forcing (ERF) for 1750-2019 (unit is W m<sup>-2</sup>) <ul> <li>best estimate</li> <li>5th percentile</li> <li>95th percentile</li> <li>100000 member Monte Carlo ensemble (HDF file)</li> </ul> </li> <li>table A3.4: future projections of ERF from 1750-2500 (including historical to 2014, projections starting from 2015). Unit is W m<sup>-2</sup>. <ul> <li>table A3.4a: SSP1-1.9 (best estimate, 5th and 95th percentile)</li> <li>table A3.4b: SSP1-2.6 (best estimate, 5th and 95th percentile)</li> <li>table A3.4c: SSP2-4.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4d: SSP3-7.0 (best estimate, 5th and 95th percentile)</li> <li>table A3.4e: SSP5-8.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4f: breakdown of minor greenhouse gases, and aggregated categories, for the five Tier 1 SSP scenarios in tables A3.4a to A3.4e (best estimate)</li> <li>tables A3.4x: tables A3.4a to A3.4f for Tier 2 SSP scenarios: <ul> <li>SSP3-7.0-lowNTCF</li> <li>SSP3-7.0-lowNTCFCH4</li> <li>SSP4-3.4</li> <li>SSP4-6.0</li> <li>SSP5-3.4-over</li> </ul> </li> </ul> </li> <li>table A3.5: projections of ERF from 1750-2500 from RCP2.6, RCP4.5, RCP6.0 and RCP8.5 using AR6 assessment (best estimate, 5th and 95th percentile, breakdown of minor gases; unit is W m<sup>-2</sup>)</li> </ul> <p><strong>Citation</strong></p> <p>IPCC, 2021: Annex III: Tables of historical and projected well-mixed greenhouse gas mixing ratios and effective radiative forcing of all climate forcers [Dentener F.J., B. Hall, C. Smith (eds.)]. In <em>Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</em> [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press.</p>
National Edition of Aldo Moro's works (RDF Dataset)
<p>A Turtle file that contains structural, intertextual and contextual data about the National Edition of Aldo Moro's works.</p>
Accurate Vertical Ionization Energy and Work Function Determinations of Liquid Water and Aqueous Solutions
<p>Dataset underlying report about a protocol to determine absolute binding energies from photoionization of liquid microjet samples, published as <a href="https://doi.org/10.1039/D1SC01908B">Accurate vertical ionization energy and work function determinations of liquid water and aqueous solutions</a>.</p>
Working memory capacity of crows and monkeys arises from similar neuronal computations
<p>Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (<em>Corvus corone</em>) in a change detection task, developed for monkeys (<em>Macaca mulatta</em>), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.</p>
Gewandhaus performance counts for composers and works 1781–1895
<p>These datasets contain lists of composers and works that were part of the Leipzig Gewandhausorchestra's repertoire in the years 1781–1895. Displayed are the performance counts and percentages for each category.</p> <p>This dataset is used in the dissertation "Repertoire and canon", <<a href="https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-810514">https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-810514</a>>.</p>
Research Integrity Promotion Work
<p>The aim of the research was to delve into the meaningfulness of such work as promotion of research integrity. The following research questions were formulated:</p> <p>1) What aspects of the role of research integrity promoter are meaningful? Why?</p> <p>2) To what changes does research integrity promotion as meaningful work lead?</p> <p>To answer these research questions, a qualitative research approach was employed using individual semi-structured interviews for data collection. The population of interest were national research integrity promoters: national ombudspersons for research integrity (coded as OMB), representatives for research integrity/misconduct from research funding organizations (RFO) and representatives from national research integrity networks (RIN) in European countries.</p> <p>Purposive sampling method was used to select informants. To identify potential informants, we used website of European Network of Research Integrity Offices (<a href="http://www.enrio.eu/">http://www.enrio.eu/</a>) and official websites of target organizations; in addition, snowball method was used to identify potential informants who corresponded to sampling criteria but were not part of the ENRIO membership. At the first stage, 32 potential informants were identified; due to missing contact information or irrelevance of activities as defined in sampling criteria, only 21 informants were invited to participate in the research. Overall, 10 national research integrity promoters (7 females and 3 males; all hold PhD degree and were or currently are part of academia) consented to take part in the research: 5 national ombudspersons for research integrity, 3 representatives for research integrity/misconduct from research funding organizations and 2 representatives from national research integrity networks.</p> <p>Semi-structured questionnaire (interview guide) consisting of five key topics (self-identity, goals, impediments to meaning, enablers to meaning, rewards for meaning-making) and the closing section was used. Interviews were conducted remotely. The interview language was English.</p> <p>Each interview was audio-recorded, transcribed, and anonymized. Average length of interview was 53 minutes; average non-anonymized interview transcript contained 6851 words.</p> <p>Each transcript was validated by two researchers for data accuracy and clarification of inaudible responses; anonymization of each transcript was validated by two researchers and a respective informant. Due to uniqueness of the national status of informants, two informants asked to change their initial consent to disclose anonymized transcript (i.e., they agreed on the use of their interview transcripts for data analysis but not on granting open access to them).</p> <p> </p> <p>The research is published as Tauginienė, L., Gaižauskaitė, I. (2022). Jumping with a Parachute – Is Promoting Research Integrity Meaningful? <em>Accountability in Research: Policies and Quality Assurance</em>. https://doi.org/10.1080/08989621.2022.2044318</p>
Spatial Scaling Challenge. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data, documentation, and scripts that compose the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> is an open exercise where we challenge the remote sensing community to retrieve relevant vegetation biophysical and physiological variables such as leaf chlorophyll content (<em>C</em><sub>ab</sub>), leaf area index (<em>LAI</em>), maximal carboxylation rate (V<sub>c</sub><sub>max,25</sub>), and non-photochemical quenching (<em>NPQ</em>) from simulated (hyperspectral reflectance (<em>HDRF</em>), sun-induced chlorophyll fluorescence (<em>F</em>) and land surface temperature (<em>LST)</em>) imagery.</p> <p>The dataset contains the simulated remote sensing and field data, their description, and scripts in Matlab, Python, and R languages to facilitate importing and handling the data and producing the standardized outputs necessary to participate.</p> <p><strong>IMPORTANT:</strong> <strong>Additional data</strong> that can be used at the discretion of the participants have been released in <strong><a href="https://doi.org/10.5281/zenodo.6530187">https://doi.org/10.5281/zenodo.6530187</a></strong></p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p>
Spatial Scaling Challenge - Additional Data. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data additional data provided for the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p> </p> <p>The main dataset, documentation and scripts of the <strong>SPATIAL SCALING CHALLENGE</strong> must be downloaded from<strong>: <a href="https://doi.org/10.5281/zenodo.6451335">https://doi.org/10.5281/zenodo.6451335</a></strong></p> <p>This additional dataset includes half-hourly time series of down-welling incoming spectral irradiance (W m<sup>-2</sup> µm<sup>-1</sup>) in the visible and near-infrared domains as measured by a field spectroradiometer operating in a nearby ecosystem station. The inclusion of these data in the Spatial Scaling Challenge is at the discretion of the participants. </p> <p> </p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p> </p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p> </p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p> <p> </p>
Science communication: How to tell the story of your scientific work
<p>Have you ever wondered why certain research projects get picked up in the news and not others? Or how some researchers manage to produce science content that goes viral on social media? Sure, part of it is luck, but another part of it is storytelling. By framing your research in a different way, you can increase the chances that your story gets picked up, or that your social media gains a following.</p> <p><a href="https://www.youtube.com/watch?v=aasLG7uOGAg">This webinar </a>will give you the tools to tell stories about your research intentionally, identifying newsworthy stories, who’s your audience, what medium best fits your story, and considering whether you want to pitch your story to journalists, or perhaps use your own media production skills, and posting it to social media. But what platform? We will cover all of this and more in part 1 of our Arctic PASSION seminar! This is part 1 of a series of seminars that Arctic PASSION will be hosting. Arctic PASSION is an EU Horizon 2020-funded project which aims to build a coherent Arctic Observing System that is adjusted to societal needs based on a co-design of knowledge.</p> <p>The Arctic PASSION Online Seminar and Dialogue Series is a tool to communicate project’s topics, share ideas, plans and results, and initiate an inclusive and proactive dialogue with people from different groups, backgrounds and career levels. It is targeted to Arctic and Indigenous Youth, Early Career Scientists and other interested audiences. The online seminar is led by Olivia Rempel, a documentary filmmaker and multimedia journalist working at GRID-Arendal, where she does everything from producing, shooting and editing documentaries, to guest teaching a mini science communication course at the Technical University of Denmark. She holds a master’s degree from the UC Berkeley Graduate School of Journalism, with prior undergraduate work in both journalism and environmental studies. Olivia has had a variety of media jobs, from logistics and communication work at Students on Ice, an educational polar expedition organization, to leading open-source investigations that combat disinformation at the UC Berkeley Human Rights Center and working on documentaries that have been screened at film festivals from Svalbard to Addis Ababa. Olivia has been working alongside passionate researchers for much of her career, and one of her greatest joys is helping them ensure their important work is communicated accurately and effectively.</p> <p>Useful Links:</p> <p>Watch this video on Youtube: <a href="https://www.youtube.com/watch?v=aasLG7uOGAg Olivia's public profile and contact details: https://www.grida.no/staff/108">https://www.youtube.com/watch?v=aasLG7uOGAg </a></p> <p>Olivia's public profile and contact details: <a href="https://www.grida.no/staff/108">https://www.grida.no/staff/108</a></p> <p>Olivia's slides: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbE13RnFpMDZiMlFMSjlOLV9oMVRfWjFrdm5Zd3xBQ3Jtc0ttWlJVZzVPMGE1djNsTGgwcmMweUNpeUkwQVhCT1FSdy1wVEFTRjdlRVhRVW41dkZmODZWbUNLc1VBeVhfNW9lYnRqTTdJOEdhc1hBUzQtV0dvcUpDQjNiYzRUV0NpSDZ3UzRrRl9mSl90c3Z3cW9hVQ&q=https%3A%2F%2Fnextcloud.awi.de%2Fs%2F2Gnj8pprcia9mD6&v=aasLG7uOGAg">https://nextcloud.awi.de/s/2Gnj8pprci...</a></p> <p>List of databases mentioned: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVNenZNcEFLV01ielJrdFVQa3hkZlpDMnYyQXxBQ3Jtc0tsNXZOT2h3VHJtNTZ0akVtdzVLNFl3Smd1dmdvN1RVSnB6VXRacHFuSGZFM3hJUGtMOHI3UXpEaXhPWnRoOGZnOFYxYXdoTXdwN2JaSTBLb0Z5bERfczh1VEFiWUFfVDFQaDdBRktPT1I1cDdpVnVLRQ&q=https%3A%2F%2Fresearchguides.journalism.cuny.edu%2Ffindingexperts%2Fdiverse-experts&v=aasLG7uOGAg">https://researchguides.journalism.cun...</a></p> <p>GRID-Arendal media resources, free for reuse: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0V3WVA2bUhHeVp5dUVLQUxRaW5pYU9UUm5jQXxBQ3Jtc0trYXBsUExha2d3QmMydzhDOEtaUjJnZU1DMElrdFE3QXV2Rmo5d0NSRXh6UWdhLTVjdC1XT0E3VkhIQUx0c193cjcwd0w4NEo4cnBFTzhfY19DYXlRR3FJTzd3VWtRZEl6dHFTOWJiVk9jekRYX1c5Yw&q=https%3A%2F%2Fwww.grida.no%2Fresources&v=aasLG7uOGAg">https://www.grida.no/resources</a></p> <p>Science communication citations: Bickford D, Posa MRC, Qie L, Campos-Arceiz A, Kudavidanage EP. Science communication for biodiversity conservation Biological conservation.. 2012 Jul;151(1):74-76. DOI: 10.1016/j.biocon.2011.12.016.</p> <p>Bullock OM, Shulman HC and Huskey R (2021) Narratives are Persuasive Because They are Easier to Understand: Examining Processing Fluency as a Mechanism of Narrative Persuasion. Front. Commun. 6:719615. doi: 10.3389/fcomm.2021.719615</p> <p>Márquez MC and Porras AM (2020) Science Communication in Multiple Languages Is Critical to Its Effectiveness. Front. Commun. <a href="https://www.youtube.com/watch?v=aasLG7uOGAg&t=331s">5:31</a>. doi: 10.3389/fcomm.2020.00031</p> <p>Pavelle S and Wilkinson C (2020) Into the Digital Wild: Utilizing Twitter, Instagram, YouTube, and Facebook for Effective Science and Environmental Communication. Front. Commun. 5:575122. doi: 10.3389/fcomm.2020.575122</p>
The Impact of Working From Home on the Success of Scrum Projects: A Multi-Method Study
<p>The number of companies opting for remote working has been increasing over the years, and Agile methodologies, such as Scrum, were adapted to mitigate the challenges caused by the distributed teams. However, the COVID-19 pandemic imposed a fully working from home context, which has never existed before. To investigate this phenomenon, we used a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through semi-structured interviews. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares - Structural Equation Modeling (PLS-SEM) through a quantitative survey of 138 software engineers who worked from home within Scrum projects. From assessing our model, we can conclude that all the latent variables are reliable and all the hypotheses are significant. We emphasize the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the ability of working from<br> home and the use of Scrum both contribute to project success, with Scrum acting<br> as a mediator.</p>
Data from two studies of learning in visual span working memory tasks.
<p>Data from two working memory span tasks. The span set size could be up to six, and each row in each .csv is one response (i.e., one "click" on an item). Thus, each trial's data is spread out on multiple rows, with accuracy being a binary variable.</p>
DATASET: Using auxiliary electrochemical working electrodes as probe during contact glow discharge electrolysis: A proof of concept study
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br> 'Using auxiliary electrochemical working electrodes as probe during contact glow discharge electrolysis: A proof of concept study'<br> (doi:10.26434/chemrxiv-2022-0v5sc).</p> <p>The data to each figure is provided in a subfolder where each curve is stored as a single CSV.<br> The filenames contain labels describing the curves.</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.