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1,607 results for “ART”
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
State of the art of studies on earthworm populations in the state of Paraná
<p>In this review, we included all the studies performed in the state of Paraná, Brazil, which had data on earthworms. We reviewed the literature for all publications (journal articles, dissertations, theses, conference proceedings, book chapters) carried out in the state of Paraná, Brazil, which had data on earthworms. The period evaluated ranged from 1986 (earliest date in the state) to 2020. Searches were performed in online databases including Sicence Direct, Scielo, CAPES and the digital collection of dissertations and theses from Brazilian Universities (BDTD). </p> <p>Overall 51 publications had earthworm data, including abundance, biomass, species, richness or just presence/absence. Data were extracted from these publications and compiled into an excel file. The dataset contains information gathered from 62 of the 399 municipalities in Paraná, and includes separation in ten geopolitical regions (IBGE 2010), as well as topographic regions and climate (Köppen, 1931). The ten geopolitical mesoregions are: West (WE), Northwest (NW), Center West (CW), Center North (CN), North Pioneer (NP), Center East (CE), Metropolitan (MT), Center South (CS), Southeast (SE) and Southwest (SW). The three topographic regions include the First, Second and Third Plateaus, and the Coastal Lowland. </p> <p>Earthworm data are presented as total abundance (number of individual m<sup>-2</sup>), fresh biomass (in g m<sup>-2</sup>) and species richness (total number). We also provide information on each species encountered, its ecological category, and whether it is native or exotic to the state of Paraná. For earthworm species, ecological category information follows the classification of Bouché (1977), including the intermediate categories: e.g., anecic, epigeic, endogeic, polyhumic endogeic, mesohumic endogeic, epi-endogeic, endo-epigeic. For each species, full names (when available), and species author(s) and year of the description are provided.</p> <p>Geographic location is provided when possible, with latitude, longitude and altitude, soil types according to the Sistema Brasileiro de Classificação de Solos - SiBCS (Santos et al. 2018). Sampling date and season are also provided, when available.</p> <p>When known, the sampling method(s) used were given. These included quantitative methods involving handsorting, such as 1) the standard Tropical Soil Biology and Fertility (TSBF) Programme method (Anderson & Ingram 1993), in which soils are handsorted from monoliths 25x25 cm square to depths ranging from 10 to 40 cm (identified as TSBF in the spreadsheets); or 2) other monolith dimensions like 20x20, 40x40 and 50x50 cm (identified as Handsorting in the spreadsheets). Qualitative sampling (e.g. Bartz et al. 2014) included: 1) collecting in various niches like deeper soil layers, litter, under rocks, in and under rotting logs, next to water bodies like streams, lakes and swamps; 2) chemical extraction using a diluted formalin solution (usually over an area 50x50 cm), following recommendations of ISO 23611-1 (2017), and pouring of the solution either on the soil surface, or at the bottom of the pit; 3) electrical extraction using a modifed apparatus (Azevedo et al. 2010), based on the Octet-Method (Thielemann 1986).</p> <p>The determination of LUS was based on Nadolny et al. (2020), which characterized Native Vegetation, Forest Plantation (including forest with <em>Pinus</em> sp. and <em>Eucalyptus</em> sp.), Pasture, Integrated Systems (e.g., agropastoral, silvopastoral or agrosilvopastoral systems) and agricultural areas (Conventional Tillage, No-Tillage and Minimum Tillage). In addition to these, Perennial Crops, Grass Lawns and Agroforestry Systems were included.</p> <p>The soil chemical and physical analysis data were included in the dataset when performed in the same places as the earthworm sampling. Chemical data included: pH, H+Al, K, Ca, Mg, P, C, sum of Bases, CEC, Base saturation, N, Na. Physical data included: sand, clay and silt proportions, texture, porosity, density and resistance to penetration.</p> <p>All data are provided in excel format and include 5 tabs: Readme, Legend, Earthworms + environment, Species distribution and References. The Readme tab provides information on the associated publication in the Revista Brasileira de Ciência do Solo authored by Dudas et al. (see https://doi.org/10.36783/18069657rbcs20220159). The Legend tab provides a description of the variables used in the other (data) tabs. Earthworms + environment has information on the sampling methods used, the earthworm abundance, biomass and richness found at the different sampling sites in Paraná, and the data on soil and environmental variables. The Species distribution tab provides information on the species found, places of origin, ecological category, sampling method used and LUS. The References tab provides detailed bibliographic information on the sources of the data used to build the tables and the dataset.</p> <p>Overall, in the state of Paraná, 90 species of earthworms were found in 51 counties of the state, of which 66 were native and 24 were exotic. A large number of species (46) are likely new and still must be formally described. Higher species richness was found in native vegetation, which also had a higher proportion of native species (75%). The other LUS with more native species were: Forest Plantation (FP) and No-Tillage (NT), while Conventional Tillage (CT) sites had only 17% native species. Earthworm abundance and biomass were highest in less disturbed LUS such as agroforestry systems, native vegetation, forestry plantation,<br> grass lawns, and permanent crops, compared to the highest disturbance LUS including soil preparation (MT and CT), where the lowest abundance and biomass were found. However, as only 16% of the 399 counties in Paraná have been sampled so far, much further research is needed in order to adequately assess the relationships between earthworms and land use. </p>
Pseudonymised Dataset of the Characterising the IIIF and Linked Art communities survey
<p>This is the pseudonymised<em> </em>dataset of the survey titled "Characterising the IIIF and Linked Art communities" that was conducted between 24 March and 7 May 2023. The survey explored the socio-technical characteristics of two prevalent community-driven initiatives in the cultural heritage domain, namely the International Image Interoperability Framework (IIIF) as well as Linked Art. The survey was carried out as part of the PhD Thesis titled "Linked Open Usable Data for Cultural Heritage: Perspectives on Community Practices and Semantic Interoperability" (see <a href="https://phd.julsraemy.ch" target="_blank" rel="noopener">https://phd.julsraemy.ch</a>).</p> <p>The survey report is available at <a href="https://hal.science/hal-04162572">https://hal.science/hal-04162572</a></p> <p>The dedicated GitHub repository is available at: <a href="https://github.com/julsraemy/loud-socialfabrics/" target="_blank" rel="noopener">https://github.com/julsraemy/loud-socialfabrics/ </a></p>
Asymmetry of AMOC Hysteresis in a State-of-the-Art Global Climate Model
<p>These directories contain Python (v3) scripts for plotting/analysing model output.</p> <p>Python scripts can be found in the directory 'Program'. Model output can be found in the directory 'Data'.</p> <p>The processed model output are stored as NETCDF files and using the relevant scripts one can regenerate all the figures. We provided the original model output (native grid) and is only converted to yearly-averaged data (due to storage limitations). Some scripts (e.g., FOV_index.py and AMOC_transport.py) use the original model output and running these script generates in the time series, which are presented in the manuscript.</p>
FIG. 5 in The equids represented in cave art and current horses: a proposal to determine morphological differences and similarities
FIG. 5. — Representation of the Magdelian horse figures studied: A-D, "Altamira"; E, F, "Niaux"; G, "Font de Gaume"; H-M, "Lascaux". Designed by Francisco Salado (IAPH), using dashed lines in some recreated parts of the figures.
FIG. 7 in The equids represented in cave art and current horses: a proposal to determine morphological differences and similarities
FIG. 7. — In current breeds of horses, the mane is long and hangs by the neck. In this picture, the "Retuertas" horse could be one of the most ancient breeds. Photo Esteban García-Viñas.
FIG. 6 in The equids represented in cave art and current horses: a proposal to determine morphological differences and similarities
FIG. 6. — Discriminant body proportion analysis of three species of modern horses show three separate groups using: A, absolute variables and B, indexes.
Abb. 5 in Eine neue Art der Gattung Quedius STEPHENS aus Syrien (Coleoptera, Staphylinidae, Staphylininae, Staphylinini, Quediina
Abb. 5: Verbreitung von Quedius fissus GRIDELLI (schwarze Dreiecke ̅ untersuchtes Material, weisse Dreiecke ̅ Literaturmeldungen), Q. skoupyi nov.sp. (grauer Kreis), Q. fagelianus COIFFAIT (weisse Rauten) und Q. sp. (weisser Kreis).
Abb. 3a-e in Eine neue Art der Gattung Quedius STEPHENS aus Syrien (Coleoptera, Staphylinidae, Staphylininae, Staphylinini, Quediina
Abb. 3a-e: Quedius fissus GRIDELLI von Syrien, Mashtal Helu (a) und Türkei, Antalya (b-e): Habitus (a); Aedoeagus, ventral (b); Aedoeagus, lateral (c); Paramere (d); Apex der Paramere (e). Massstäbe: 1 mm (a-d), 0,2 mm (e).
Abb. 1a-e in Eine neue Art der Gattung Quedius STEPHENS aus Syrien (Coleoptera, Staphylinidae, Staphylininae, Staphylinini, Quediina
Abb. 1a-e: Quedius skoupyi nov.sp. (♂-Holotypus): Habitus (a); Aedoeagus, ventral (b); Aedoeagus, lateral (c); Paramere (d); Apex der Paramere (e). Massstäbe: 1 mm (a-d), 0,2 mm (e).
Abb. 1-6 in Eine neue Art der Scirtes flavoguttatus-Gruppe aus Malaysia (Coleoptera, Scirtidae) (223. Beitrag zur Kenntnis der Scirtidae)
Abb. 1-6: (1) Scirtes geberti nov.sp., Habitus, dorsal; (2) Scirtes geberti nov.sp., 9. Sternit; (3) Scirtes geberti nov.sp., 8. Tergit; (4) Scirtes geberti nov.sp., 8. Tergit, Hinterrand; (5) Scirtes geberti nov.sp., 9. Tergit; (6) Scirtes geberti nov.sp., Aedoeagus.
ERC Locus Ludi. Images en jeu: transferts et continuité. Droits des images et de la recherche (Histoire de l'Art et Archéologie)
<p>Martine Denoyelle, conservatrice en chef du patrimoine, éminente spécialiste de la céramique grecque et en particulier des productions à figures rouges de Grande Grèce, explore les enjeux liés aux droits des images dans la recherche.</p> <p>More about Locus Ludi: www.locusludi.ch</p>
How Software Organizations are using the ISO/IEC 29110 Standard's Processes: A Survey of the State of the Art and Practice
<p>ISO/IEC 29110 was developed containing a set of industrially validated practices that can potentially be adopted by software Very Small Entities (VSE). VSEs usually have characteristics that differentiate them from organizations of different sizes, such as extremely limited resources and informal project management processes, tending to adopt Agile methods and having an historical resistance to the adoption of standards, that are in general developed for large organizations. In this sense, our research question arises: "How are software organizations using the ISO/IEC 29110 practices?". To answer this question, a Systematic Mapping Study (SM), and a Survey with software organizations were carried out in order to identify the state of the art and the state of the practice in relation to the use of the standard’s practices. The SM identified 21 primary studies reporting the use of the standard in hundreds of software organizations with positive results such as organizational learning, process improvement, improved communication, and also some negative results, such as deployment difficulties in technical areas and the need for additional time and resources. The Survey carried out with 23 software companies identified that, in general, companies do not explicitly know the content of the standard, but partially carry out, in accordance with the standard, practices related to planning, monitoring, control and execution of a project plan, and do not carry out requirements analysis or architecture and detailed design as defined in the standard. </p>
Greek Children Art Museum dataset
<p>The "LD-project" deals with the creation of cultural Open Linked Data (LOD), based on the artifacts of Greek Children Art Museum, in Greece, Athens. It was created in the frame of Master Thesis of MSc. students, Marini Efstathia and Chondrogianni Maria, in Cultural Infomatics postgraduate program at the Department of Cultural Technology and Communication of the University of the Aegean.</p> <p>DCAT Description: <a href="https://www.childrensartmuseum.gr/LD-project/">https://www.childrensartmuseum.gr/LD-project/</a></p>
The influence of art expertise and training on emotion and preference ratings for representational and abstract artworks.
<p>Across cultures and throughout recorded history, humans have produced visual art. This raises the question of why people report such an emotional response to artworks and find some works more beautiful or compelling than others. In the current study we investigated the interplay between art expertise, and emotional and preference judgments. Sixty participants (40 novices, 20 art experts) rated a set of 150 abstract artworks and portraits during two occasions: in a laboratory setting and in a museum. Before commencing their second session, half of the art novices received a brief training on stylistic and art historical aspects of abstract art and portraiture. Results showed that art experts rated the artworks higher than novices on aesthetic facets (beauty and wanting), but no group differences were observed on affective evaluations (valence and arousal). The training session made a small effect on ratings of preference compared to the non-trained group of novices. Overall, these findings are consistent with the idea that affective components of art appreciation are less driven by expertise and largely consistent across observers, while more cognitive aspects of aesthetic viewing depend on viewer characteristics such as art expertise.</p>
Supplementary material 1: Reviewer Comments from: Widening the circle of care: An arts-based, participatory dialogue with stakeholders on cancer care for First Nations, Inuit, and Métis peoples in Ontario, Canada - Research Ideas and Outcomes 2: e9115 (25 May 2016) https://doi.org/10.3897/rio.2.e9115
The attached file includes the evaluation of the postdoctoral fellowship application from three reviewers. Guidelines for reviewers are available online for more inforamtion (http://www.cihr-irsc.gc.ca/e/33043.html), including the rating scale that is used to score each section of the evaluation.
Photonics4All - OmniLightLaboratory: the art of light
<p>This video is presenting the OmniLight Laboratory, a tool which was developed by the International Laser Center in Slovakia within the EU funded project Photonics4All.</p>
Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
<p>This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities. The dataset is intended for use in machine learning model testing and algorithm validation. The properties calculated include solvation energies using the COSMO-RS method [1] and ionization potentials and electron affinities using various high-accuracy computational methods as implemented in MOLPRO [2]. Computational details can be found in Ref. [3], with scripts used to generate the data mostly uploaded to our github repository [4].</p> <p>Molecular Datasets Considered:</p> <ul> <li> <p>QM9 Dataset: Contains small organic molecules broadly relevant for quantum chemistry [5]</p> </li> <li> <p>Electrolyte Genome Project (EGP): Focuses on materials relevant to electrolytes.[6]</p> </li> <li> <p>GDB17 and ZINC databases: Offer a broad chemical diversity with potential application in battery technologies. [7, 8]</p> </li> </ul> <h2>Data structure</h2> <p>How to Load the Data:</p> <p>All files can be loaded with</p> <p><br><code>import json</code></p> <p><code>with open("file.json", "r") as f:</code><br><code> data_dict = json.load(f)</code></p> <p><br>and the filestructure can be explored with</p> <p><code>data_dict.keys()</code></p> <p>We have also added an example script in python that shows how to extract all data from the JSON files following this link</p> <p><a href="https://github.com/chemspacelab/VienUppDa/blob/main/SolQuest/BIG_MAP_DATA/load_db.py">How to extract the data</a></p> <p>Note the file structure of the the AMONS JSON files is slightly different as explained below!</p> <h3>Solvation energies</h3> <p>The data is stored in two types of JSON archives: files for full molecules of GDB17 and ZINC and files for amons of GDB17 and ZINC. They are structured differently as amon entries are sorted by the number of heavy atoms in the amon (e.g., all amons with 3 heavy atoms are stored in <code>ni3</code>). Because of the large number of amons with 6 or 7 heavy atoms,they are further split into <code>ni6_1</code>, <code>ni6_2</code>, and so on. A sub dictionary of an amon dictionary or a full molecule dictionary contains the following keys:</p> <p><code>ECFP</code> - ECFP4 representation vector</p> <p><code>SMILES</code> - SMILES string</p> <p><code>SYMBOLS</code> - atomic symbols</p> <p><code>COORDS</code> - atomic positions in Angstrom</p> <p><code>ATOMIZATION</code> - atomization energy in [kcal/mol]</p> <p><code>DIPOLE</code> - dipole moment in Debye</p> <p><code>ENERGY</code> - energy in Hartree</p> <p><code>SOLVATION</code> - solvation energy in [kcal/mol] for different solvents at 300 K.</p> <p> </p> <p>Files:</p> <p> </p> <p><strong><em><code>GDB17.json.zip</code> </em></strong>(unpack with unzip first with unzip <strong><em><code>GDB17.json.zip</code></em></strong>) - subset of GDB17 random molecules</p> <p><strong><em><code>AMONS_ZINC.json</code> </em></strong>-<strong><em> </em></strong>all<strong><em> </em></strong>amons of ZINC up to 7 heavy atoms</p> <p><strong><em><code>EGP.json</code> </em></strong>- EGP molecules</p> <p><code><strong><em>AMONS_GDB17.json</em></strong></code> - all amons of GDB17 up to 7 heavy atoms</p> <p><code><strong>QM9IPEA_raw_molpro_output</strong>.zip</code> - compressed folder with raw Molpro input and output files</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description </strong></td> <td><strong>Molecules</strong></td> </tr> <tr> <td>AMONS_GDB17.json</td> <td>GDB17 amons</td> <td>37860</td> </tr> <tr> <td>AMONS_ZINC.json</td> <td>ZINC amons </td> <td>88771</td> </tr> <tr> <td>GDB17.json</td> <td>Subset of GDB17</td> <td>309468</td> </tr> <tr> <td>EGP.json </td> <td>EGP molecules </td> <td>18362</td> </tr> </tbody> </table> <p>Atomic energies $E_{at}$ at BP and def2-TZVPD level in Hartree [Ha]</p> <table> <tbody> <tr> <td><strong>Element</strong></td> <td><strong>H</strong></td> <td><strong>C</strong></td> <td><strong>N</strong></td> <td><strong>O</strong></td> <td><strong>F</strong></td> <td><strong>Br</strong></td> <td><strong>Cl</strong></td> <td><strong>S</strong></td> <td><strong>P</strong></td> </tr> <tr> <td>Eat [Ha]</td> <td>-0.5</td> <td> -37.85</td> <td> -54.60</td> <td> -75.09</td> <td>-99.77</td> <td>-2574.40</td> <td> -460.20</td> <td> -398.16</td> <td>-341.30</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>B</strong></td> <td><strong>Si</strong></td> </tr> <tr> <td> -24.65</td> <td> -289.40</td> </tr> </tbody> </table> <p>We follow the convention of negative atomization energies for stablity compared to the isolated atoms:</p> <p>$E_{atomization} = E_{mol} - \sum_{i} E_{at,i}$</p> <p><br>Free energy of solvation at 300 K in [kcal/mol]:</p> <h3>Ionization potentials and electron affinities</h3> <p>The upload contains two JSON files, <strong><em>QM9IPEA.json</em></strong> and <strong><em>QM9IPEA_atom_ens.json</em></strong>. <strong><em>QM9IPEA.json </em></strong>summarizes MOLPRO calculation data grouping it along the following dictionary keys:</p> <p> </p> <p><strong>QM9IPEA.json</strong></p> <p><code>COORDS</code> atom coordinates in Angstroms<br><code>SYMBOLS</code> atom element symbols<br><code>ENERGY</code> total energies for each charge (0, -1, 1) and method considered<br><code>CPU_TIME</code> CPU times (in seconds) spent at each step of each part of the calculation<br><code>DISK_USAGE</code> highest total disk usage in GB<br><code>ATOMIZATION_ENERGY</code> atomization energy at charge 0 (all methods)<br><code>IONIZATION_ENERGY</code> ionization energy for all methods<br><code>ELECTRON_AFFINITY</code> electron affinity for all methods<br><code>HOMO_ENERGY</code> HOMO energy from DFHF calculations<br><code>LUMO_ENERGY</code> LUMO energy from DFHF calculations<br><code>QM9_ID</code> ID of the molecule in the QM9 dataset</p> <p><strong>QM9IPEA_atom_ens.json</strong></p> <p><code>SPINS</code> the spin assigned to elements during calculations of atomic energies<br><code>ENERGY</code> energies of atoms using different methods</p> <p> </p> <p> </p> <p>All energies are given in Hartrees with NaN indicating the calculation failed to converge. Ionization potentials and electron affinities can be recovered as energy differences between neutral and charged (+1 for ionization potentials, -1 for electron affinities) species.</p> <p>"CPU_time" entries contain steps corresponding to individual method calculations, as well as steps corresponding to program operation: "INT" (calculating integrals over basis functions relevant for the calculation), "FILE" (dumping intermediate data to restart file), and "RESTART" (importing restart data). The latter two steps appeared since we reused relevant integrals calculated for neutral species in charged species' calculations; we also used restart functionality to use HF density matrix obtained for the neutral species as the initial density matrix guess for the SCF-HF calculation for charged species. NaN CPU time value means the step was not present or that the calculation is invalid. Note that the CPU times were measured while parallelizing on 12 cores and were not adjusted to single-core.</p> <p><strong> </strong></p> <p><strong><em>QM9IPEA_atom_ens.json</em></strong> contains atomic energies used to calculate atomization energies in <strong><em>QM9IPEA.json</em></strong>, the dictionary keys are:</p> <p><code>SPINS</code> - the spin assigned to elements during calculations of atomic energies.</p> <p><code>ENERGY</code> - energies of atoms using different methods.</p> <p> </p> <p>(Note that H has only one electron and thus does not require a level of theory beyond Hartree-Fock.)</p> <p>NOTE: Additional calculations were performed between publication of arXiv:2308.11196 and creation of this upload. For the version of the dataset used in the manuscript, please refer to DOI:10.5281/zenodo.8252498.</p> <h3>Acknowledgement</h3> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY 2030+). O.A.v.L. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772834). O.A.v.L. has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. O.A.v.L. acknowledges that this research is part of the University of Toronto’s Acceleration Consortium, which receives funding from the Canada First Research Excellence Fund (CFREF). Obtaining the presented computational results has been facilitated using the queueing system implemented at <a href="https://leruli.com">https://leruli.com</a>. The project has been supported by the Swedish Research Council (Vetenskapsrådet), and the Swedish National Strategic e-Science program eSSENCE as well as by computing resources from the Swedish National Infrastructure for Computing (SNIC/NAISS).</p> <p> </p> <h3>References</h3> <p>[1] Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172, 43–72</p> <p>[2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schutz, M. Molpro: a general-purpose quantum chemistry program package. WIREs Comput. Mol. Sci. 2012, 2, 242–253</p> <p>[3] arxiv link of draft</p> <p>[4] <a href="https://github.com/chemspacelab/ViennaUppDa">https://github.com/chemspacelab/ViennaUppDa</a></p> <p>[5] Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014, 1, 140022</p> <p>[6] Qu, X.; Jain, A.; Rajput, N. N.; Cheng, L.; Zhang, Y.; Ong, S. P.; Brafman, M.; Mag- inn, E.; Curtiss, L. A.; Persson, K. A. The Electrolyte Genome Project: A big data approach in battery materials discovery. Comput. Mater. Sci. 2015, 103, 56–67</p> <p><strong> </strong>[7] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enu- meration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 2012, 52, 2864–2875</p> <p>[8] Irwin, J. J.; Shoichet, B. K. ZINC A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 2005, 45, 177–182.</p>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
Fig. 2-7 in Zur Kenntnis der Scirtidae des Iran mit Beschreibung je einer neuen Art aus den Gattungen Cyphon P , 1799 und Elodes L , 1796 (Coleoptera) (175. Beitrag zur Kenntnis der Scirtidae)
Fig. 2-7: Cyphon pareuoplus n. sp., 2: 9. Sternit, 3: 8. Tergit, 4: 9. Tergit, 5: Tegmen, 6: Penis, 7: Penis, Prostheme.
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.