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Fig. 2. Diapsid Palacrodon browni Broom, 1906 in First records of diapsid Palacrodon from the Norian, Late Triassic Chinle Formation of Arizona, and their biogeographic implications
Fig. 2. Diapsid Palacrodon browni Broom, 1906 (PEFO 37247) from Norian, Petrified Forest National Park, Arizona, USA; in occlusal (A), labial (B), lingual (C), and oblique (D) views. Arrows point in anterior direction. Photographs (A1, B1, C, D), explanatory drawings (A2, B2).
Fig. 1 in First records of diapsid Palacrodon from the Norian, Late Triassic Chinle Formation of Arizona, and their biogeographic implications
Fig. 1. Geographic and stratigraphic context of Palacrodon occurrences in Arizona. A. Map of Arizona showing the locations of PFV 396 and MNA 207-2. Map of North America modified from www.lakodosajta.info. B. Stratigraphic chart of the Chinle Formation at PEFO and the Placerias Quarry, with detrital zircon dates from Ramezani et al. (2011).
Fig. 2. Juvenile Orthacanthus platypternus, Stephanian B in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 2. Juvenile Orthacanthus platypternus, Stephanian B (Upper Carboniferous) of Hamilton, USA (A) compared to juvenile of O. bohemicus, Westphalian D (Upper Carboniferous) of Bohemia, Czech Republic (B), showing the relative position of the dorsal spine. A, modified from Zidek (1993b: fig. 1); B, modified from Soler-Gijón (2004: fig. 3C).
Fig. 3 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 3. External morphology of dorsal spines of Orthacanthus platypternus (Cope, 1884), Lower Permian, Craddock Bone Bed, Texas, USA. A. HMNS-T1, juvenile, lateral view. B. SMU 68799, juvenile, posterior view. C. SMU 68800, juvenile, posterior (C1) and postero-lateral (C2) views. D. SMU 68801, adult, denticulated, postero-lateral view (D1) and non-denticulated, posterior view (D2) regions. Numbers 1 to 17 point to the positions of the denticles along the posterior sides of the spines. Numbers 1' to 17' correspond to the right row of denticles. Grey areas in B–D2 represent sedimentary matrix. Scale bars 5 mm.
Fig. 6 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 6. Cross-sections of dorsal spines (non-denticulated region) of Orthacanthus platypternus (Cope, 1884), Lower Permian, Craddock Bone Bed, Texas, USA. Comparison between the smallest, specimen HMNS-T1 (A), and the largest Orthacanthus dorsal spine sampled, specimen HMNS-J1 (B). Posterior sides of the spines are pointing to the top of the figure.
Fig. 9 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 9. Bivariate plots of relationship between width and height in cross sections of Orthacanthus dorsal spines. A. Orthacanthus platypternus (Lower Permian, Craddock Bone Bed, Texas, USA). B. Comparison of O. platypternus with O. meridionalis (Upper Carboniferous, Puertollano, Spain) and Orthacanthus sp. (Upper Carboniferous, Robinson, Kansas, USA). Note the linear regression (y = 0.91x + 0.15; n = 65, r2 = 0.98) calculated by Donelan and Johnson (1997) for the isolated dorsal spines of O. platypternus. The maximum and minimal values are also included here for a comparative reference; values of the rest of the specimens are not included for clarity. Grey shaded areas indicate approximate intervals of the three size clusters corresponding to juveniles and adults according the biometric analysis by Donelan and Johnson (size intervals are based on unpublished data presented in a poster at 57th Annual Meeting of the Society of Vertebrate Paleontology, Chicago, October 1997). Note the ontogenetic trajectories of several individuals of O. platypternus (HMNS-T2 and HMNS-J3), O. meridionalis (PU-XE19, 20, 74 and 76) and Orthacanthus sp. (KUVP-72324) showing stages SP1–n spine proper). Data from O. meridionalis and Orthacanthus sp. after Soler-Gijón (1999: table 1 and figs. 4–8). Vertical grey arrows point to the position in the linear regression for SMU specimens according the maximum width values at the proximal end of denticulated regions: 3.5 mm (SMU 68799), 4.5 mm (SMU 68800) and 8.4 mm (SMU 68801). Abbreviations: SP1–5, spines 1–5; SpPr, spine proper.
Fig. 8. A in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 8. A. Reconstruction of the dorsal spine (mainly the denticulated region) of Orthacanthus platypternus, based on specimens HMNS-T1 and HMNS-T2. B, C. Reconstructions of the dorsal spine of Orthacanthus meridionalis (modified from Soler-Gijón 1999: fig. 10); PU-XE76 (B) PU-XE19 (C). Note that distal denticles belong to the first spine (juvenile). The intersection of the first major growth line and the denticulated border of the spine indicate the proximal end of the denticulated region of the first spine; the following "spines" exhibit the distal denticles and those corresponding to each new growth stage.
Fig. 5 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 5. Serial cross-sections (A, C, D) of dorsal spine of Orthacanthus platypternus (Cope, 1884), Lower Permian, Craddock Bone Bed, Texas, USA; specimen HMNS-J3, where A represents the most proximal section and D is the most distal. B. Detail of section in A showing three minor growth lines close to the base of the right denticle in the figure (arrows).
Fig. 4 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 4. Serial longitudinal sections of dorsal spine of Orthacanthus platypternus (Cope, 1884), Lower Permian, Craddock Bone Bed, Texas, USA; specimen HMNS-T2, where A represents the most proximal section and E is the most distal. Samples C–E include the denticulate region. Photos of the sections (A1–E1), interpretative drawings (A2–E2). Scale bars 1 mm.
Fig. 7 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 7. Cross-section of dorsal spine of Orthacanthus platypternus (Cope, 1884), Lower Permian, Craddock Bone Bed, Texas, USA; specimen HMNS-J7 non-denticulated region) showing color-banding in centrifugal trabecular dentine and growth lines in lamellar dentine. A. General view (posterior side of spine towards the top of the figure). B, C. Details of periphery showing pairs of growth lines (marked by arrows) in the centrifugal lamellar dentine. D. Detail of interglobular spaces within the centripetal lamellar dentine (arrows denote interglobular spaces).
Fig. 1 in Morphology and histology of dorsal spines of the xenacanthid shark Orthacanthus platypternus from the Lower Permian of Texas, USA: Palaeobiological and palaeoenvironmental implications
Fig. 1. Baylor County map (A) and stratigraphic section (B) at the Craddock Ranch (denoted with a star), Seymour Texas, lower Clear Fork Formation (cf. Nelson et al. 2013). Numbers 1–5 denote units through the sampled portion of the Craddock Ranch exposure.
Fig. 2 in Relative growth of freshwater prawn Macrobrachium brasiliense (Decapoda, Palaemonidae) and its implications for reproduction
Fig. 2. Scatterplots of the morphometric relationships for males of Macrobrachium brasiliense (Heller, 1862) (CL, carapace length; PW, pleura width; IL, ischial length; ML, merus length; CRL, carpus length; PPL, propodus length; DCL, dactYl length; PPH, propodus height; measurements in mm).
Fig. 1 in Relative growth of freshwater prawn Macrobrachium brasiliense (Decapoda, Palaemonidae) and its implications for reproduction
Fig. 1. Structures selected for morphometric analYsis (CL, carapace length; PW, pleura width; IL, ischial length; ML, merus length; CRL, carpus length; PPL, propodus length; DCL, dactYl length; PPH, propodus height; measurements in mm). Adapted from MELO (2003).
Fig. 3 in Relative growth of freshwater prawn Macrobrachium brasiliense (Decapoda, Palaemonidae) and its implications for reproduction
Fig. 3. Scatterplots of the morphometric relationships for females of Macrobrachium brasiliense (Heller, 1862) (CL, carapace length; PW, pleura width; IL, ischial length; ML, merus length; CRL, carpus length; PPL, propodus length; DCL, dactYl length; PPH, propodus height; measurements in mm).
FIGURE 10 in Comparison of Miocene to early Pleistocene-aged Castor californicus (Rodentia: Castoridae) to extant beavers and implications for the evolution of Castor in North America
FIGURE 10. Dendrogram of cranial hierarchical cluster analysis. Specimens used in analysis are labeled by species and catalog number.
Data and code for "Carbon cycle instability for high-CO2 exoplanets: implications for habitability"
<p>Data supporting "Carbon cycle instability for high-CO2 exoplanets: implications for habitability," Graham and Pierrehumbert, 2024, <em>Astrophysical Journal </em>(under review at time of upload)</p> <ul> <li>Global climate model (GCM) simulation output necessary to reproduce figures in paper. Netcdf (.nc) format. <ul> <li>For simulations receiving instellation of 1250 W m-2, the format is atmos_monthly_[co2 mixing ratio]ppmv_S1250.nc</li> <li>For other simulations, the format is: atmos_monthly_[co2 partial pressure]bar_S[675, 750, 800, or 1000].nc</li> </ul> </li> <li>Continental configuration land mask used in GCM simulations. Netcdf (.nc) format. <ul> <li>land.nc</li> </ul> </li> <li>Python scripts to<br> <ul> <li>post-process climate simulation data to calculate global weathering rates according to WHAK or MAC weathering <ul> <li>isca_weathering_calc.py</li> </ul> </li> <li>reproduce all model-based figures <ul> <li>figures 1-8: isca_plot_maker.py</li> <li>figure 9: isca_weathering_calc.py</li> </ul> </li> </ul> </li> </ul>
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>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Data for Water inputs across the Namib Desert: implications for dryland edaphic microbiology
<p>These are the data files required to run the analyses in Water inputs across the Namib Desert: implications for dryland edaphic microbiology.</p>
Summary statistics from "Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease"
<p>GWAMA summary statistics of four steroid hormone levels using fixed-effect model and GWAS summary statistics of four other steroid hormones.</p> <p>When using this data, please cite: Pott J, Bae YJ, Horn K, et al.. Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease. <em>J Clin Endocrinol Metab</em> <strong>2019</strong> Nov 1;104(11):5008-5023. doi: 10.1210/jc.2019-00757</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>effect_allele</li> <li>other_allele</li> <li>effect_allele_freq</li> <li>min_info (minimal info score across all used studies)</li> <li>n (sample size per SNP)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>CochransQ (only in GWAMA; SNP heterogeneity across studies)</li> <li>pCochransQ (only in GWAMA; p-value of Cochrans Q value)</li> </ul>
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.