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Figure 3 in Revision of the A. delicata group of Afriodinia d'Abrera, 2009 (Papilionoidea: Riodinidae) with the description of a new species from Mount Lico, Northern Mozambique, and the reinstatement of a species
Figure 3 – Comparison of the uncus, gnathos and valve of Afriodinia species and subspecies. a,b,c. Afriodinia lico: ABRI-2018-4657; Mt Lico, Zambesia, Mozambique; 15°47.538'S; 37°21.783'E, 1000 m; 15–16.v.2018; J. Bayliss. d,e. Afriodinia lico: ABRI-2018-4648; otherwise as for a,b,c. f,g,h. Afriodinia zanzibarica: ABRI-2021-0023; Jozani Forest, Zanzibar, Tanzania; 6.2417°S; 39.4111°E, 12 m; ix.1990; S.C.Collins. i,j,k. Afriodinia zanzibarica: ABRI-2021-0025; otherwise as for f.c.h. but iv.1994; S.C.Collins. l,m,n. Afriodinia delicata delicata: ABRI-2018-4658; River Ruo, Mt Mlanje, Malawi; 15.95°S; 3.5617°E, 900 m; ii.1983; S.C. Collins. p,q,r. Afriodinia delicata tanzania; ABRI-2021-0024; Udzungwa Mts, Tanzania; 7.80°S 36.88°E, 1000 m; iii.2000; T.C.E. Congdon, I. Bampton, P.Walwanda, M. Hassan.
Figure 5 in Revision of the A. delicata group of Afriodinia d'Abrera, 2009 (Papilionoidea: Riodinidae) with the description of a new species from Mount Lico, Northern Mozambique, and the reinstatement of a species
Figure 5 – Direct photographic comparison of the aedeagi of a. Afriodinia lico: ABRI-2018-4657 b. Afriodinia lico: ABRI-2018-4648; c. Afriodinia zanzibarica: ABRI-2021-0023 d. Afriodinia delicata delicata: ABRI-2018-4658. Label data as per Fig. 3.
Figure 1 in Revision of the A. delicata group of Afriodinia d'Abrera, 2009 (Papilionoidea: Riodinidae) with the description of a new species from Mount Lico, Northern Mozambique, and the reinstatement of a species
Figure 1 – Map showing study area between Malawi and Mozambique, and location of Mount Lico in relation to neighbouring mountains above 1000m
The data for: LCR in fungi display functional groups and are depleted in positively charged amino-acids
<p>Abstract</p> <p>The dataset consists of a TAB formatted table (Main_Dataset.tsv), that integrates information about protein domains (pfam_scan, GO terms), low complexity regions (SEG), signal peptides (SignalP), and transmembrane elements (TMHMM) for each of the analysed proteins within 183 fungal proteomes. The Main Dataset has been designed to ease further searches with Linux bash commands, for e.g. sorting and subsetting by the aforementioned traits. This resource can be used by users interested in detailed annotation of particular protein families, sets of organisms, low complexity regions, types of proteins (for instance transmembrane proteins). Thanks to its simple and clear format, it may also be easily enriched with additional data.</p> <p>Data types:</p> <p>Main_Dataset.tsv is a TSV table with protein annotations</p> <p>Estimate of dataset size:</p> <p> 245MB<br><br>Readme file:</p> <p>Main_Dataset.tsv is a TSV table with the following columns:</p> <ol> <li> <p>Assembly ID from NCBI</p> </li> <li> <p>Protein ID (NCBI accession)</p> </li> <li> <p>Protein length</p> </li> <li> <p>Presence of protein domains; Boolean</p> </li> <li> <p>symbolic localization of protein domains; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>number of transmembrane elements predicted with TMHMM</p> </li> <li> <p>total length of transmembrane elements</p> </li> <li> <p>Symbolic localization of transmembrane elements; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>Presence of signal peptide; Boolean</p> </li> <li> <p>Total number of LCR</p> </li> <li> <p>Total length of LCR</p> </li> <li> <p>Symbolic localization of LCR; 3 bins: N-termini (0-0.25 of protein length), middle (0.25-0.75 of protein length), and C-term (0.75-1 protein length)</p> </li> <li> <p>Symbolic localization of LCR; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>LCR sequences in the N-terminal part of protein, separated by a comma</p> </li> <li> <p>LCR sequences in the middle part of protein, separated by a comma</p> </li> <li> <p>LCR sequences in the C-terminal part of the protein, separated by a comma</p> </li> <li> <p>Pfam domains overlapping with LCRs (>80% of LCR length)</p> </li> <li> <p>Pfam domains in protein (ordered by domain start)</p> </li> <li> <p>GO terms based on Pfam domains obtained by mapping on pfam2go, separated with the pipe symbol '|'</p> </li> </ol> <p> </p> <p>Acknowledgements</p> <p>This work was supported by National Science Centre grants (#2021/41/B/NZ2/02426 to AM, #2019/35/D/NZ2/03411 to K.S).</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>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Data accompanying publication "High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide."
<p>This dataset accompanies the publication "How High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide." </p> <p>In our study, we combine income-based emission inequality data with an emulator-based modeling framework to thoroughly study the link between emissions of individual, wealthy emitter groups and climate extremes worldwide. Specifically, we assess individual contributions to current global temperature levels and systematically attribute changes in regional monthly heat and drought extremes across the globe.</p> <p>We focus on emissions of the top 10/1/0.1 wealthiest individuals globally and in the US, the EU27, India and China. The dataset contains results for 1-in-50/100/10'000 year extremes at grid-cell level and whenever imapcts are aggregated by region we refer to the regionmask AR6 regions. </p> <p>The file contents are the following:</p> <ol> <li>Attributed_GMT.csv: attributed global mean temperature levels by emitter group</li> <li>tas_frequency_hot.nc, spei_frequency_dry.nc, spi_frequency_dry.nc: attributed changes in the frequency of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>tas_intensity_hot.nc, spei_intensity_dry.nc, spi_intensity_dry.nc: attributed changes in the intensity of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>processed_extremes_frequency.csv: attributed changes in the frequency of extreme events aggregated to ar6 land regions </li> <li>processed_extremes_intensity.csv: attributed changes in the intensity of extreme events aggregated to ar6 land regions</li> </ol>
Discrimination Study_ILD_Group_Raw Data
<p>The updated version of ILD (Interaural Level Difference) training group experimental data from <em>Binaural-Cue Reweighting Induced by Discrimination Training</em> have been uploaded, accompanied by a README files that describes the data attributes for PreTraining followed by Assessment (Pre/Posttest) and Training.</p>
Fig. 64 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Fig. 64. Distribution map of Achilia cosmoptera group. (¤ squares edged in blue) A. angularis. (l red circles) A. elfridae. (r triangles edged in blue) A. melanocephala. (š circles edged in green) A. pandemica. (« fuchsia stars) A. quarantena n. sp. (J green squares) A. temporalis.
Fig. 63 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Fig. 63. Distribution map of the Achilia cosmoptera species group. (q blue inverted triangles) A. bifrons. (u green diamonds) A. blanchardi. (J red square) A. cosmoptera. (p blue triangles) A. covidia n. sp
Figs 51-56 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 51-56. (51, 53, 55) Achilia elfridae. (52, 54, 56) A. pandemica n. sp. Male head in (51-52) dorsal, (53-54) lateral and (55-56) frontal views. Scale bars (200 μm) left for (51, 53, 55) and right for (52, 54, 56).
Figs 37-42 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 37-42. (37, 39, 41) Achilia bifrons. (38, 40, 42) A. blanchardi. Male head in (37-38) dorsal, (39-40) lateral and (41-42) frontal views. Scale bars (200 μm) left for (37, 39, 41) and right for (38, 40, 42).
Figs 33-36 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 33-36. Achilia angularis. Male head in (33) dorsal, (34) lateral, (35) semifrontal, and (36) frontal views. Scale bar = 200 μm.
Figs 16-22 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 16-22. Male antennae of Achilia. (16) A. covidia. (17) A. blanchardi. (18) A. bifrons. (19) A. temporalis. (20) A. pandemica. (21) A. angularis. (22) A. quarantena.
Figs 43-48 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 43-48. (43, 45, 47) Achilia cosmoptera. (44, 46, 48) A. covidia n. sp. Male head in (43-44) dorsal, (45-46) lateral and (47-48) frontal views. Scale bars (200 μm) left for (43, 45, 47) and right for (44, 46, 48).
The predication of soil nematode and functional groups abundance in the terrestrial on the Tibetan Plateau (Data Set)
<p>This is the dataset which is generated from the manuscript entitled 'The predication of soil nematode and functional groups abundance in the terrestrial on the Tibetan Plateau'.</p> <p>The spatial resolution of this dataset is about 1 km, of which coordinates systems is WGS84.</p> <p>There are 6 layers of nematode abundance in this GeoTiff file:</p> <p>1. abd - Total Soil Nematode Abundance</p> <p>2. bact - Abundance of bacterivores</p> <p>3. fung - Abundance of fungivores</p> <p>4. herb - Abundance of plant parasite</p> <p>5. omni - Abundance of omnivores</p> <p>6. pred - Abundance of predators</p>
Fig. 63 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Fig. 63. Distribution map of the Achilia cosmoptera species group. (q blue inverted triangles) A. bifrons. (u green diamonds) A. blanchardi. (J red square) A. cosmoptera. (p blue triangles) A. covidia n. sp.
Dataset accompanying the journal article: Bouldering psychotherapy is not inferior to cognitive behavioural therapy in the group treatment of depression: A randomized controlled trial
<p>SPSS-Dataset containing the (not-imputed) raw data for the non-inferiority trial on BPT vs. CBT. All personal data removed. Only data of participants of the CBT or BPT group included.</p>
Figs 23-32 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 23-32. Male protibiae (23-25), mesotibiae (26-31) and metatibiae (32) of Achilia. (23, 28) A. elfridae. (24, 30, 32) A. angularis. (25, 31) A. quarantena. (26) A. covidia. (27) A. blanchardi. (29) A. pandemica.
Figs 7-15 in A revision of the Chilean Brachyglutini - Part 7. Revision of Achilia Reitter, 1890: A. cosmoptera species group (Coleoptera: Staphylinidae: Pselaphinae)
Figs 7-15. Aedeagi (7, 8, 13), their internal sacs (9-12), and profemora (14, 15) of Achilia. (7, 14) A. quarantena. (8-12, 15) A. angularis, specimens from Malleco, Nahuelbuta National Park. (10) A. angularis, type from Chillan. (13) A. pandemica.
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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.