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51 results for “Work analysis”
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"
<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>
Polish is quantitatively different on quartzite flakes used on different worked materials [R analysis]
<p>This upload includes the following files related to the R analysis:</p> <p>- Raw data as a CSV table (processing-quartzite-final.csv), i.e. results from the ConfoMap analysis (see <a href="https://doi.org/10.5281/zenodo.3979116">https://doi.org/10.5281/zenodo.3979116</a>)</p> <p>- RStudio project (Quantification quartzite final.Rproj)</p> <p>- R scripts as R Markdown files (*.Rmd)</p> <p>- R scripts knitted to HTML files (*.html)</p> <p>- An R script (RStudioVersion.R) to write the used version of RStudio to a text file (RStudioVersion.txt)</p> <p>- Output from script #1: processing-quartzite-final.Rbin and processing-quartzite-final.xlsx</p> <p>- Output from script #2: processing-quartzite-final_summary-stats.xlsx</p> <p>- Output from script #3: all plots as PDF files.</p> <p>Note that for running the scripts, the raw data files (processing-quartzite-final.csv, .Rbin and .xlsx) should be stored in a "Data" folder within the working directory.<br> Output (processing-quartzite-final_summary-stats.xlsx and PDF plots) were saved into "Summary-stats" and "Plots" folders, respectively.</p> <p>Zenodo does not allow sub-folders, so this folder structure had to be removed.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Polish is quantitatively different on quartzite flakes used on different worked materials [ConfoMap analysis]
<p>Each surface has been processed with two templates:</p> <p>1) Extract two 50x50 µm sub-areas and extract topography layer from each sub-area. Export sub-areas as SUR files. File names start with "A35" or "VSH4".</p> <p>2) Process all extracted sub-areas for quantitative analysis. File names start with "processing-quartzite-final".</p> <p>All ConfoMap templates are saved in MNT format (including all original and processed surfaces, as well as results). Each template has also been exported to a PDF file.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p> <p>Additionally, the results of the second template are collated into "processing-quartzite-final.csv".</p>
Replication package for the paper "A configurational approach to job quality analysis: forms of inequalities at work in Europe"
<p>The following replication package is appended to the article <span><span><span><span>Étienne Penissat</span><span>, </span></span><span><span>Cécile Rodrigues</span><span> & </span></span><span><span>Alexis Spire</span></span></span></span> <span>(2024)</span> "<span>A configurational approach to job quality analysis: forms of inequalities at work in Europe",</span> <span>European Societies,</span> <span>DOI: <a href="https://doi.org/10.1080/14616696.2024.2312950">10.1080/14616696.2024.2312950</a></span></p> <p>The scripts to be run in the following order are:</p> <p>- 1_Penissat_EuropeanSocieties_2023_DataPreparation.R : Recoding, formatting and scope of data used</p> <p>- 2_Penissat_EuropeanSocieties_2023_DataAnalysis.Rmd : Analysis and statistical results</p> <p>The data used in the article comes from the EWCS (2015) - European Working Condition Survey - provided by the European foundation for the improvement of living and working conditions. The data is not available on free access but can be obtained on request. Information on the survey wave used can be found here : https://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015</p> <p>- In the first "1_Penissat_EuropeanSocieties_DataPreparation.R" script, the file containing data named "ewcs_1991-2015.dta" is used. The file called "eseg2_trad.csv" contains english labels for the nomenclature of professional positions ESeG. As "ewcs_1991-2015.dta" is not freely available, it is not included in the package and "eseg2_trad.csv" is located in the "data" folder.</p> <p>- The first script creates the data file "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" in the "results" folder.</p> <p>- The second script called "2_Penissat_EuropeanSocieties_DataAnalysis.Rmd" uses the "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" data file and produces the "2_Penissat_EuropeanSocieties_DataAnalysis.html" file containing all code and results presented in the article.</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>
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>
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Generation of tissue-like models of human bilayered tissues functionalized with olive oil components"
<p>This file contains the raw dataset generated in the work entitled "GENERATION OF NOVEL TISSUE-LIKE MODELS OF HUMAN BILAYERED TISSUES FUNCTIONALIZED WITH BIOACTIVE COMPONENTS OBTAINED FROM OLIVE OIL". These results correspond to the quantification of the histological results obtained in this work.</p>
Quantitative results of the analysis of two types of bone particles corresponding to the work "A comprehensive analysis of two types of xenogeneic bone particles for use in maxillofacial bone regeneration therapies"
<p>This dataset corresponds to the quantitative data generated in the work entitled "A comprehensive analysis of two types of xenogeneic bone particles for use in maxillofacial bone regeneration therapies".</p> <p>Regeneration of maxillofacial bone structures is challenging. One of the strategies applied to bone damage repair is the use of bone filler particles, and different types of these particles have been tested. In this work, we analyzed the regenerative potential of deproteinized bone particles (DP) and collagen-based bone particles (CP) to determine the potential of each biomaterial in bone repair. Results of the structural analysis using scanning electron microscopy and 3D scanning showed that DP and CP were structurally similar, and consisted of a heterogeneous mixture of bone particles of different sizes and shapes. Then, ex vivo analyses using morphological evaluation, LIVE & DEAD and quantification of DNA released to the medium demonstrated that CP and DP were highly biocompatible when used in direct and in indirect contact with human cells, at 24, 48 and 72h of follow-up. Then, when both particles were grafted for 2 months on Wistar rats in which a critical defect had been generated at the mandible bone. Results of the computed tomography analysis showed a significant reduction of the bone defect in the CP group, but not in the DP group, as compared with negative controls devoid of any bone particles. Histological analysis of the graft area revealed that both particles were biocompatible in vivo, and a regenerative tissue with collagen fibers and mineralized spots was found in CP and DP, with higher number of mineralized spots in DP. Histochemistry and immunohistochemistry analyses confirmed the presence of collagen, proteoglycans and osteocalcin at the regeneration area of CP and DP. In general, these results confirm the biocompatibility of both types of particles and that both were able to induce maxillofacial bone regeneration, especially in the case of CP. Future studies should determine their clinical usefulness in patients with cleft palate, mandibular damage and other maxillofacial applications.</p>
Polish is quantitatively different on quartzite flakes used on different worked materials [Python analysis]
<p>This upload includes the following files related to the Python analysis:<br> 1. Raw data as a XLSX table (processing-quartzite-final-2020-04-29.xlsx) is the output from R Script #1 (see <a href="https://doi.org/10.5281/zenodo.3979139">https://doi.org/10.5281/zenodo.3979139</a>), even though the filename is slightly different.</p> <p>Plus, for each analysis (full and restricted datasets), included in the corresponding ZIP archive:<br> 2. Jupyter notebooks of the analysis (Classification_RandSplitFeature_Revision_VXX.ipynb) rendered to HTML file (Classification_RandSplitFeature_Revision_VXX.html)<br> 3. Dataframe including the artificially filled datapoints<br> 4. Output of the analysis as PDF:<br> • Confusion matrices ("CM")<br> • Decision trees on selected features ("DecisionTreeSel")<br> • Balanced accuracy vs. maximum depth ("depth")<br> • Mutual information ("MI")<br> • Pairplots of selected features ("pairplot")<br> • Performance on training sets ("performance")</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet
<p>The information was further refined by adding color: pink indicates “project” (4 entries), blue shows “repository” (14 entries), yellow is “journal” (7 entries) and green for “hybrid” (1 entry). Future versions of the spreadsheet are planned, which can be filtered via other categories, for example: discipline-specific vs discipline-agnostic, funding model, level of editing/intervention, etc.</p> <p>The collection and analysis of data took place between 1 February and 30 June 2015</p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet FINAL
<p><strong>NB: This dataset is superseded by:</strong></p> <p>Murphy, Fiona et al.. (2015). WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED. Zenodo. 10.5281/zenodo.33899</p> <p>Data Publishing Workflows collected and analysed between 1 February - 30 June 2015. Fields were populated using a combination of consultation and desk research. This is a refined version of the previous spreadsheet also lodged in Zenodo: </p> <p>Murphy, Fiona et al.. (2015). WDS-RDA Publishing Data Workflows Working Group Analysis sheet. Zenodo. 10.5281/zenodo.19107</p> <p>Publication date: 29 June 2015</p> <p>Keyword(s): <strong>data publishing, workflows, journals, repositories, research data</strong></p>
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></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>
A Linguistic View of Shakespeare Works Based on Text Analysis
Open the record for dataset details and reuse information.
MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors
<p>Supplementary Material for Poster Presentation</p> <p>MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors</p> <p>Conference: 15th MOH-AMM Scientific Meeting 2024 in conjunction with 25th NIH Scientific Conference</p>
Comparative analysis of huntingtin interaction partner database searches with Dr. Maiuri's work - 2018/08/31
<p><strong>Project </strong>- Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale</strong> - To compare the ROS-specific huntingtin interaction partners identified by Dr. Tamara Maiuri with those detailed in existing databases. </p> <p><strong>Overview</strong> - Previously, different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Following completion of this initial analysis, Dr. Tamara Maiuri posted in her open notebook a detailed list of high and medium confidence ROS-specific huntingtin interacting proteins: <a href="https://zenodo.org/record/1319540">https://zenodo.org/record/1319540</a>. A comparison of these interactors with those identified in the previously mined databases is briefly detailed. </p>
The Effects of Cycle and Treadmill Desks on Work Performance and Cognitive Function in Sedentary Workers: data repository of a review and meta-analysis.
<p>This repository contains additional files related to the review and meta-analysis. The first dataset contains list of search terms. The second dataset contains list od studies included in the meta-analysis. The third dataset contains study evaluation using PEDro scale tool. The fourth dataset contains two additional forrest plots (Effect of cycle and treadmill desks on typing errors and Effect of cycle and treadmill desks on congruent Eriksen Flanker test).</p>
Enhancing Collaborative Care in Schizophrenia: A Comparative Analysis of ICF Core Sets Assessments by Occupational Therapy and Mental Health Social Work Students
<p>This repository contains the R code, dataset, and README file for the network analysis of ICF (International Classification of Functioning, Disability, and Health) assessments. The study compares the assessments conducted by occupational therapy students and mental health social work students, analyzing centrality measures and Bridge Expected Influence within their respective networks.</p>
Global Trends in research of work-related musculoskeletal disorders among surgeons: Bibliometric analysis and visualization from 1991 to 2024
<p><span><span>This file contains all the information relating to the 184 articles included in the bibliometric analysis.</span></span></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.