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13,499 results for “researcher”
Figure 1 in Cretaceous marine amniotes of Australia: perspectives on a decade of new research
Figure 1. Stratigraphical distribution of Australian Cretaceous marine amniote taxa updated from Kear (2003). Australian standard microplankton (dinoflagellate) zonation is modified from Partridge (2006) to accommodate the emended geological timescale of Gradstein et al. (2012). Taxon ranges indicate named species (black bars) or indeterminate occurrences assigned to higher-level taxa (open bars).
Figure 3 in Cretaceous marine amniotes of Australia: perspectives on a decade of new research
Figure 3. Marine amniote fossils from Cretaceous strata in Australia. A, elasmosaurid premaxillary palate (SAM P40510) exposing the vomerine contact and intracranial sinus. B, spectacular mounted skeleton (QM F18041) of the new polycotylid popularly dubbed the 'Richmond pliosaur'. C, partially disarticulated 'juvenile' postcranium referred to Umoonasaurus demoscyllus. Both scapulae (outlined) and an in situ gastrolith mass are indicated. D, 'Umoonasaurus-like' propodial from the late Aptian Darwin Formation, Northern Territory. E, CT rendering of an exceptionally preserved 'juvenile' Platypterygius australis cranium and mandible (AM F98273). Image compilation: Ben Hill (Adelaide). F, articulated humerus and distal forelimb elements (AM F107444) of a 'juvenile' Platypterygius australis. G, ophthalmosaurian phalanx (WAM 99.1.4) from the late Cenomanian Geale Siltstone, Western Australia. Image: Mikael Siversson (Western Australian Museum). H, mosasaurid ulna (UWA 37092) with antebrachial foramen and intermedium contact indicated. I, cranium of Bouliachelys suteri (SAM P41106) in lateral view. J, articulated cranium and carapace of Bouliachelys suteri (SAM P40525) in dorsal view. Scale bars represent 20 mm in A, G, H; 500 mm in B; 100 mm in C, J; and 50 mm in D–F, I. Abbreviations: abf – antebrachial foramen; dfi – distal facet for the intermedium; ics – intracranial sinus; gst – gastrolith mass; hpx – hooked premaxillae; lea – lateral exposure of angular; pmj – premaxillary, maxillary, and jugal contacts; rbe – reduced basioccipital extracondylar area; rze – position of radial zeugopodial element; scp – scapulae.
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Dataset: GH Research PLC (GHRS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Acacia Research Corporation (ACTG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ACM Research, Inc. (ACMR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Acacia Research Corporation (ACTG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ACM Research, Inc. (ACMR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Research Solutions, Inc. (RSSS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Research Frontiers Incorporated (REFR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: National Research Corporation (NRC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Lam Research Corporation (LRCX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)
<p>Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)</p> <p>Contains publicly available data from:</p> <ul> <li>Higher Education Research Institute. 1966–2006. “Cooperative Institutional Research Program(CIRP) Data Archives of the Freshman Survey Trends from 1966 to 2006.”</li> <li>National Center for Science and Engineering Statistics. 1972–1990. “Higher Education Research and Development Survey (HERD).</li> <li>U.S. National Center for Education Statistics. 1993. 120 Years of American Education: A Statistical Portrait. Washington, DC:U.S. Department of Education.</li> <li>U.S. National Center for Education Statistics. 2005. “Digest of Education Statistics, 2005.”</li> <li>U.S. National Center for Education Statistics. 2019. “Digest of Education Statistics, 2019.”</li> </ul> <p>To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", unzip all folders in this repository and place all files and folders (except "PapersFieldsofStudy.txt.gz") in "data/raw". AEA Data and Code Repository openicpsr-204361 shows the expected file structure. </p> <p>Additionally, this repository contains "PapersFieldsofStudy.txt.gz" from Microsoft Academic Graph data (April 2018 Extract) used for "Undergraduate Gender Diversity and the Direction of Scientific Research." To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", please decompress and place "PapersFieldsofStudy.txt.gz" into "data/raw/MAG/". The associated code repository can be found AEA Data and Code Repository openicpsr-204361.</p> <p>The attribution license of the Microsoft Academic Graph is <a href="https://opendatacommons.org/licenses/by/1-0/">ODC-BY</a></p> <p>Please cite the following paper in publications and reports using the Microsoft Academic Graph:</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p> <p> </p>
Images from field research in Cambodia - October/November 2022 for the TEX-KR project
<p>These pictures were taken by Magali An Berthon for the EU-funded MSCA TEX-KR project, as part of a period of fieldwork in Phnom Penh, Cambodia in October-November 2022. Following copyright restrictions for this institution, this repository only shows a selection of images from the Tuol Sleng Genocide Museum. It includes images of the public exhibition Remembering S-21 Victims through their Clothes: Textile Preservation at Tuol Sleng Museum; images from a workshop (October 24-27, 2022) on textile research and curatorial practices led by Magali An Berthon and interpreter Moeung Seyha with the textile conservation team at Tuol Sleng Genocide Museum and two conservators from the National Museum of Cambodia; and finally images from the museum permanent galleries, especially the rooms showcasing paintings of artist Vann Nath (1946-2011). </p> <p>All these images must be credited © Magali An Berthon for TEX-KR / courtesy of Tuol Sleng Genocide Museum.</p>
Research data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach"
<div><strong>Research Data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em></strong></div> <div> </div> <div>Dear reader,</div> <div> </div> <div>reasearch data are provided for the research article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em> (https://doi.org/10.1016/j.advwatres.2024.104763). The authors hope that the research data allows for a better understanding of the modeling workflow. The research data covers the following files:</div> <div> <ul> <li>Python scripts to create the models <ul> <li>Model scripts using FloPy (Bakker et al., 2016) are stored as .py files in './model_data/flopy_scripts/', named 'model_variant_vXYZ.py', where 'XYZ' is a wildcard for the model number. </li> <li>--> Note that model numbers correspond to the different model variants as referred to in the article, see overview below.</li> <li>The model scripts require postfix files, stored in './model_data/flopy_scripts/postfix/', a PHREEQC database file, stored in './model_data/flopy_scripts/template_database/', as well as spreadsheets that contain the initial concentrations as well as reaction rate parameters needed by PHT3D, stored as .xlsx files in './model_data/flopy_scripts/', to create the models.</li> <li>Note that the .xlsx files are used by PHT3D-FSP in the model scripts to generate relevant PHT3D input files (compare https://doi.org/10.5281/zenodo.7559750 for more details).</li> </ul> </li> <li>SEAWAT/PHT3D input files <ul> <li>Original SEAWAT and PHT3D input files, which were created with the corresponding model scripts previously (see step before).</li> <li>Input files are stored in './model_data/model_files/vXYZ/model_files/' for each model variant, where 'XYZ' is a wildcard for the model number.</li> <li>SEAWAT/PHT3D executables can directly run the model files files. Thus, the files don't need to be re-created via the previous step.</li> </ul> </li> <li>Model outputs <ul> <li>Model output data is stored as NumPy arrays in './model_data/model_files/vXYZ/npy_arrays/', where 'XYZ' is a wildcard for the model number.</li> <li>The script './model_data/flopy_scripts/template_output/pht3d_output_hpc_v006.py' was used to generate the output files.</li> <li>2-D species concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/species/', where 'XYZ' is a wildcard for the model number.</li> <li>Species min./max. concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/min_max/', where 'XYZ' is a wildcard for the model number.</li> <li>2-D water budget arrays (CH & WEL boundaries) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/budgets/', where 'XYZ' is a wildcard for the model number.</li> <li>Model discretization information (ncol, nrow, nlay etc.) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/discretization/', where 'XYZ' is a wildcard for the model number.</li> </ul> </li> <li>Figure files <ul> <li>Original figure files as well as the corresponding Python scripts to create the figures are stored in the subfolder'./figures'.</li> </ul> </li> </ul> <p>Numbering of the model variants is as follows:<br><br>v401 --> VAR-conservative<br>v402 --> VAR-OM<br>v403 --> VAR-C/I<br>v404 --> VAR-C/I/S<br>v405 --> VAR-C/I/P<br>v406 --> VAR-C/I/P/H<br>v407 --> VAR-C/I/P/V<br>v408 --> VAR-C/I/P-Co<br>v409 --> VAR-all<br>v410 --> VAR-all (no C)</p> </div> <div> </div> <div>Literature:</div> <div> </div> <div>Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J.J. and Fienen, M.N., 2016. Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), pp.733-739. https://doi.org/10.1111/gwat.12413</div> <div> </div> <div>Seibert, S.L., Massmann, G., Meyer, R., Post, V.E.A., Greskowiak, J., 2024. Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2024.104763</div> <div> </div> <div><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Vincent E.A. Post (vincent@edinsi.nl), Rena Meyer (rena.meyer@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</div>
"Towards Responsible Publishing" - Online researcher survey dataset
<p><a href="https://www.research-consulting.com/" target="_blank" rel="noopener">Research Consulting</a> and the <a href="https://www.cwts.nl/" target="_blank" rel="noopener">Centre for Science and Technology Studies (CWTS)</a> were commissioned to determine to what extent the vision, mission and objectives set out in the TRP proposal serve the needs of the global research community. Through a consultative approach, we sought to assess whether there is appetite for the type of change proposed by cOAlition S in the TRP proposal. In addition, we aimed to understand how the ‘Towards Responsible Publishing’ proposal may be modified or refined to ensure it resonates with the research community and sees broad adoption; identify any showstoppers or unintended consequences and propose proactive measures to mitigate them, ensuring successful implementation; and ascertain whether the existing scholaly communication infrastructure can support this proposal and if not, identify where research funders can strengthen the infrastructure. </p> <p>As part of the consultation, the Centre for Science and Technology Studies and Research Consulting developed a researcher survey to gather researcher feedback on aspects of the scholarly communication system. This deposit provides our anonymised survey data. Please note that country information is available for all countries with at least 50 responses; in all other cases, the country has been replaced with “Other”.</p>
Dataset of "Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxidese"
<p>With the growing interest of the electrochemical community in high-throughput (HT) experimentation as a powerful tool in accelerating materials discovery, the implementation of HT methodologies and the design of HT workflows has gained traction. We identify 6 aspects essential to HT workflow design in electrochemistry and beyond to ease the incorporation of HT methods in the community’s research and to assist in their improvement. We study IrCo mixed-metal oxides (MMOs) for the oxygen evolution reaction (OER) in acidic media using the mentioned aspects to provide a practical example of possible workflow design pitfalls and strategies to counteract them. </p>
Figure 11 in French research on fisheries in the Northwest Atlantic, from its origins to the present
Figure 11. – Results of a 10-minute dredging on the Saint-Pierre Bank Iceland scallop beds aboard the R/V Cryos in 1974 (Photo A. Forest).
Figure 10. – Plankton sampling aboard the R in French research on fisheries in the Northwest Atlantic, from its origins to the present
Figure 10. – Plankton sampling aboard the R/V Cryos in 1975 to study herring pre-recruitment on George's Bank as part of an ICNAF project (Photo A. Forest).
Figure 8 in French research on fisheries in the Northwest Atlantic, from its origins to the present
Figure 8. – Study of the Gulf of St. Lawrence cod stock: results of a 30-minute trawl haul aboard the R/V Cryos in 1977 (Photo A. Forest).
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.