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FIGURE 5 in Three decades of Chondrichthyan research in Brazil assessed from conferences' abstracts: patterns, gaps, and expectations
FIGURE 5 | Women versus men as last authors in Chondrichthyan Evolution. Orange, women; blue, men.
Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate their Absorption Strength and Onset of Absorption
<p>The uploaded datasets contain complex refractive indices of polymer thin films and a glass substrate and are published in connection with the following article:</p> <p>Kamptner, Scharber, Schiek.<br>Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate their Absorption Strength and Onset of Absorption.<br>ChemPhysChem 2024.</p> <p><a href="https://doi.org/10.1002/cphc.202400233">https://doi.org/10.1002/cphc.202400233</a></p> <p> </p> <p><strong>F8BT</strong> (or PFBT): Poly(9,9-dioctylfluorene-alt-benzothiadiazole).</p> <p><strong>MDMO-PPV</strong> (or OC1C10-PPV): Poly-[2-(3,7-dimethyloctyloxy)-5-methyloxy]-para-phenylene-vinylene.</p> <p><strong>PBDB-T-2F</strong> (or PBDB-T-F, PBDB-TF, PM6): Poly[(2,6-(4,8-bis(5-(2-ethylhexyl-3-fluoro)thiophen-2-yl)-benzo[1,2-b:4,5-b’]dithiophene))-alt-(5,5-(1’,3’-di-2-thienyl-5’,7’-bis(2-ethylhexyl)benzo[1’,2’-c:4’,5’-c’]dithiophene-4,8-dione)].</p> <p><strong>PDCBT</strong>: Poly[2,2''''-bis[[(2-butyloctyl)oxy]carbonyl][2,2':5',2'':5'',2'''-quaterthiophene] -5,5'''-diyl].</p> <p><strong>PTB7</strong>: Poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl][3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]].</p> <p><strong>ZZ50</strong> (or c-PCPDTBT): Poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)].</p> <p><strong>float glass</strong> objective slide (Marienfeld) "tin" and "air" side.</p>
GAP interatomic potential for C- and H-based systems
<p> </p> <p>This is a general-purpose Gaussian approximation potential (GAP [1]) for carbon and hydrogen based materials (CH). The potential is capabe of simulating various materials and molecules composed of C and H elements. The interatomic potential has been fitted with <strong>QUIP/GAP</strong> [1,2] using an extensive dataset of different configurations at the PBE level of theory [3] using the VASP code [4,5]. The dataset contains following structures :</p> <ul> <li>Dimers of carbon and hydrogen</li> <li>Trimers</li> <li>CH containing "soup" structures generated during iterative training</li> <li>QM9[6] molecules augmented to C and H containing molecules only</li> <li>Interactive molecules generated using active learning</li> <li>a-C dataset from [7]</li> <li>Bulk and surface carbon structures</li> <li>CH structures geneated using high pressure </li> </ul> <p>This potential includes van der Waals (vdW)<strong> </strong>corrections at the Tkatchenko-Scheffler (TS) level of theory [8] via a machine learning based local parametrization of dispersion interactions [9]. </p>
Dataset for CH GAP interatomic potential
<p>This is a dataset used to train general-purpose CH GAP interatomic potential [1].</p> <p>The database contains the following entries:</p> <ul> <li>Dimers of carbon and hydrogen</li> <li>Trimers</li> <li>"Soup" structures generated during iterative training</li> <li>QM9[6] molecules augmented to C and H containing molecules only</li> <li>Interactive molecules generated using active learning</li> <li>a-C dataset from [2]</li> <li>Bulk and surface carbon structures</li> <li>CH structures geneated using high pressure </li> </ul>
Subset of stochastically generated interacting molecules for CH GAP interatomic potential
<p>This is a subset of the dataset used to train general-purpose CH GAP interatomic potential [1].</p> <p>This subset contains the interacting molecules generated stochastically in a following manner. The subset is generated using active learning and uncertainty-based configuration selection. We started with randomly chosen pairs of CH-containing molecules from the QM9 database up to 7 carbon atoms. The probability of selecting the molecules is set based on the energy and size of the molecule. The probability is lower as the energy above the convex hull is higher. A bigger size of the molecule also lowers the probability to favor the inclusion of small structures. Then, we estimate the uncertainties for the new structures based on how far away from the existing interacting molecules in the training set they are (in configuration space), and identify those with the largest expected errors. This way, we generated about 3k structures. </p>
Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"
<p><strong>Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"</strong></p> <p><em>Original Research Article</em><br><strong>Frontiers in Toxicology</strong>, March 8, 2024<br>Section: Regulatory Toxicology<br><strong>Volume 6 - 2024</strong> | <a href="https://doi.org/10.3389/ftox.2024.1285768" target="_new" rel="noopener">https://doi.org/10.3389/ftox.2024.1285768</a></p> <p><strong>Authors</strong>:<br>Thomas Jaylet, Thibaut Coustillet, Nicola M. Smith, Barbara Viviani, Birgitte Lindeman, Lucia Vergauwen, Oddvar Myhre, Nurettin Yarar, Johanna M. Gostner, Pablo Monfort-Lanzas, Florence Jornod, Henrik Holbech, Xavier Coumoul, Dimosthenis A. Sarigiannis, Philipp Antczak, Anna Bal-Price, Ellen Fritsche, Eliska Kuchovska, Antonios K. Stratidakis, Robert Barouki, Min Ji Kim, Olivier Taboureau, Marcin W. Wojewodzic, Dries Knapen, Karine Audouze</p>
TABLE 2 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
<p><b>TABLE 2 |</b> Statistics of the multiple linear regression between the year of description of endemic fish species and altitude and fish body size (Cross-species analysis). P values in bold indicate significant results (p <0.05). SE = Standard error.</p><table><tbody><tr><th></th><th><b>Estimate</b></th><th><b>SE</b></th><th><b>t value</b></th><th><b>p value</b></th></tr></tbody><tbody><tr><th><b>Intercept</b></th><td>1991.44</td><td>2.378</td><td>837,28</td><td><0.001</td></tr><tr><th><b>Altitude</b></th><td>8.03</td><td>2.445</td><td>3.28</td><td><b>0.001</b></td></tr><tr><th><b>Fish body size</b></th><td>-11.05</td><td>2.445</td><td>-4.52</td><td><0.001</td></tr></tbody></table>
TABLE 1 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
<p><b>TABLE 1 |</b> Statistics of the linear regression between the number of descriptions of endemic fish species and the sampling effort (Cross-sites analysis). P value in bold indicate significant results (p <0.05). SE = Standard error.</p><table><tbody><tr><th></th><th><b>Estimate</b></th><th><b>SE</b></th><th><b>t value</b></th><th><b>p value</b></th></tr></tbody><tbody><tr><th><b>Intercept</b></th><td>-0.22</td><td>0.079</td><td>-2.88</td><td>0.004</td></tr><tr><th><b>Sampling effort</b></th><td>0.29</td><td>0.03</td><td>8.91</td><td><0.001</td></tr></tbody></table>
Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"
<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>
Validaded Spectral Gaps and Bands for the Critical Almost Mathieu Operator
Open the record for dataset details and reuse information.
Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context
<p>1. An ever-expanding human population, climatic changes, and the spread of intensive farming practices is putting increasing pressure on agroecosystems and their inherent biodiversity. Non-production vegetation elements, such as woody patches, riparian margins, and restoration plantings, are vital for conserving agroecosystem biodiversity. Further, such elements are key building blocks that are manipulated via land management, thereby influencing the biotic and abiotic processes that underpin functioning agroecosystems.</p> <p>2. Despite this critical role, there has been a lack of synthesis on which types of vegetation elements drive and/or support ecological processes, and the mechanisms by which this occurs. Using a systematic, quantitative literature review of 342 articles, we asked: what are the effects of non-production vegetation on agroecosystem processes and how are these processes measured within global agroecosystems?</p> <p>3. Woody patches, hedgerows and borders, riparian margins, and shelterbelts were the most studied types of non-production vegetation. The majority (61%) of studies showed positive effects of non-production vegetation on ecological processes, where the presence, level or rate of the studied process was increased or enhanced.</p> <p>4. However, four key research gaps were revealed: (1) most studies (83%) used proxies for, instead of direct measurements of, ecosystem processes related to non-production vegetation; (2) study designs used to investigate non-production vegetation effects on ecosystem processes directly were largely limited to observational comparisons of non-production vegetation types, farm-scale vegetation configurations, and different proximities to vegetation in terms of the effect on ecological processes; relatively few studies used manipulative experiments (3) the relatively few studies directly measuring ecosystem processes were dominated by four process categories: invertebrate biocontrol, predator and natural enemy spillover, animal movement, and ecosystem cycling, and (4) the methods used to directly measure non-production vegetation effects comprised a surprisingly limited set of approaches.</p> <p>5. To fill key research gaps that will inform the use of non-production vegetation to enhance agroecosystem processes, we present a framework for future research that emphasises the need to combine an understanding of human decision making with carefully-designed and targeted investigations into the roles of taxa, ecosystem processes, and landscape heterogeneity related to non-production vegetation, at multiple spatial scales within agroecosystems.</p>
GAP_workshop_1_materials
<p>Materials for ASBS 2021 conference workshop 1: HybPiper. Contains example output data from HybPiper, and HybPiper-RBGV</p>
Dataset: Backwater rise due to jams with a lower gap in an experimental flume
<p>This dataset includes flow measurements and wood accumulation characteristics of flume experiments conducted at the Hydro-environmental Resarch Centre, Cardiff University, Cardiff, UK.</p>
GAP_workshop_3_materials
<p>This archive contains the data needed to run the analysis for the GAP_workshop 3 in the ASBS 2021 conference workshop series.</p> <p>It contains the raw data needed to run all analyses and pre-prepared output files for anyone to follow the workshop in case their analysis runs into problems or takes too long.</p>
Data for: Flying through gaps – How does a bird deal with the problem and what costs are there?
<p>Animals flying in the wild often show remarkable abilities to negotiate obstacles and narrow openings in complex environments. Impressive as these abilities are, this must result in costs in terms of impaired flight performance. In this study, I used a budgerigar as model for studying these costs. The bird was filmed in stereo when flying through a wide range of gap widths from well above wingspan down to a mere 1/4 of wingspan. 3D flight trajectories were acquired and speed, wingbeat frequency and accelerations/decelerations were calculated. The bird used two different wing-postures to get through the gaps and could use very small safety margins (down to 6mm on either side) but preferred to use larger when gap width allowed. When gaps were smaller than wingspan flight speed was reduced with reducing gap width down to half for the smallest and wingbeat frequency was increased. I conclude that flying through gaps potentially comes with multiple types of cost to a bird of which the main may be: <i>i</i>) reduced flight speed increases the flight duration and hence the energy consumption to get from point A to B, <i>ii</i>) the underlying U-shaped speed to power relationship means further cost from reduced flight speed, and associated with it <i>iii</i>) elevated wingbeat frequency includes a third direct cost.</p>
Figure 5 in Global panorama of studies about freshwater oligochaetes: main trends and gaps
Figure 5. Percentage of works on freshwater oligochaetes published between 1985 and 2015 by continent (A) and by countries (15 leading ones in number of publications) (B).
Figure 3 in Global panorama of studies about freshwater oligochaetes: main trends and gaps
Figure 3. Percentage of works on freshwater oligochaetes published between 1985 and 2015 by area of knowledge (A) and 10-year totals (B). The "Others" category contains works related to oligochaetes as food for fish and used in reduction of activated sludge.
Figure 2 in Global panorama of studies about freshwater oligochaetes: main trends and gaps
Figure 2. Number of works on freshwater oligochaetes published between 1985 and 2015 (A) and 10-year totals (B).
Figure 1 in Global panorama of studies about freshwater oligochaetes: main trends and gaps
Figure 1. Total number of exclusive and shared publications on oligochaetes in freshwater habitats found in each database entre 1985 e 2015.
GAP interatomic potential for amorphous carbon
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>amorphous carbon</strong> [1]. It has been fitted with <strong>QUIP/GAP</strong> [1,2] by recomputing the <strong>a-C database of Deringer and Csányi</strong> [3] at the <strong>PBE+MBD</strong> level of theory [4,5] using the VASP code [6,7,8]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>3-body</strong> (angle_3b) descriptors [3] plus <strong>SOAP-type descriptors</strong> (soap_turbo) [9,10], as implemented in the <strong>TurboGAP</strong> code [11]. The files can be used both with QUIP/GAP (compiled with the TurboGAP libraries) and TurboGAP. More details will follow in a scientific publication in due course (bibligraphical data will be added as it becomes available).</p> <p>Changes introduced in version 2 of this potential:</p> <ul> <li>More dimer configurations</li> <li>More graphite configurations</li> <li>A tabulated "core potential" to account for short-range repulsion and long-range dispersion interactions explicitly</li> </ul> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>LibAtoms: <a href="http://libatoms.github.io">https://libatoms.github.io</a></li> <li>V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).</li> <li>J.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996).</li> <li>A. Tkatchenko, R.A. Di Stasio, R. Car, and M. Scheffler, Phys. Rev. Lett. 108, 236402 (2012).</li> <li>VASP: <a href="http://vasp.at">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>T. Bucko, S. Lebègue, T. Gould, and J.G. Ángyán, J. Phys.: Condens. Matter 28, 045201 (2016).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi">http://turbogap.fi</a></li> </ol>
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.