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opencc-by-4.0Dec 2023View details →

Database of best practice for pondscape NbS for CC adaptation and mitigation

We built an inventory (database) of Nature-based Solutions (NbS) actions (creation, restoration, and management) in ponds and pondscapes (ponds at the landscape scale) in a diversity of social-ecological settings to assess the best practices. We formulated an online questionnaire that was shared with pond stakeholders. The questionnaire asked general (e.g., number of ponds, area of the pondscape, etc.) and specific (e.g., costs of the action, stakeholders involved, etc.) information on the NbS action implemented, and on 11 associated Nature's Contributions to People (NCPs). Among the NCPs we included, for instance, habitat creation for biodiversity, regulation of climate, learning or physical and physiological experiences. The database contains information gathered through the questionnaire, research papers and relevant web pages and platforms. We used three different approaches to obtain information on NbS actions implemented in ponds/pondscapes and the associated NCPs mainly focusing on Europe and Uruguay: 1) the development of a user-friendly online questionnaire on NbS implemented in ponds/pondscapes and associated NCPs, which was shared in the form of a survey through the platform Survey Monkey with PONDERFUL members and pond Stakeholders; 2) the search of information in research papers; and 3) the search of information on web pages such as <a href="https://oppla.eu/" target="_blank" rel="noopener">https://oppla.eu</a>, <a href="https://renature-project.eu/" target="_blank" rel="noopener">https://renature-project.eu</a>, <a href="https://climate-adapt.eea.europa.eu/" target="_blank" rel="noopener">https://climate-adapt.eea.europa.eu</a>, <a href="https://una.city/" target="_blank" rel="noopener">https://una.city</a>. We requested permissions from the respondents to make the data available.

opencc-by-4.0Oct 2020View details →

Community Established Best Practice Recommendations for Tephra Studies-from Collection through Analysis

Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, extremely high-resolution time-stratigraphic markers across a range of sedimentary settings and are used in a range of disciplines (e.g., volcanology, climate science, archaeology, ecology, and impact assessment). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that often inhibits data integration across geographic regions and across disciplines. Here we present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to serve as guidelines for future investigators and to ensure that sufficient data are gathered for transparency and interoperability.  Recommendations include standardized field and laboratory data collection along with reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development.  This new standardized framework promotes consistent tephra documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers.  Wider adoption will help to expand the applicability and usability of tephra data and facilitate scientific collaboration and data reuse. For additional details, see the accompanying manuscript: Wallace, K.*, Bursik, M. Kuehn, S., Kurbatov, A., Abbott, P., Bonadonna, C., Cashman, K., Davies, S., Jensen, B., Lane, C., Plunkett, G., Smith, V. Tomlinson, E., Thordarsson, T., and Walker, D. Community established best practice recommendations for tephra studies—from collection through analysis. Sci Data 9, 447 (2022). <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a> *corresponding author:  Kristi Wallace, <a href="mailto:kwallace@usgs.gov">kwallace@usgs.gov</a> Open access article is available online here <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a> or as a PDF here <a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41597-022-01515-y.pdf&data=05%7C01%7Ckwallace%40usgs.gov%7C673f9f39fd3e4122dd9b08da6f3b9667%7C0693b5ba4b184d7b9341f32f400a5494%7C0%7C0%7C637944598967375940%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=%2BKVfwK2FbUKAoJf2gMerCmBMEQE1rvMDkS6xIk3DGKY%3D&reserved=0">https://www.nature.com/articles/s41597-022-01515-y.pdf</a>.

opencc-by-4.0Jun 2022View details →

ROSEWOOD4.0 Best practices & innovations: CSV file

ROSEWOOD4.0 harnesses digital solutions and knowledge transfer along the forest value chain to reinforce the sustainability of forest resilience and wood mobilisation in Europe. This CSV file includes the complete information of 279 Factsheets of Best practices and Innovations (BP&I) in forest management, wood supply and forest-based industries exploiting relevant digital technologies and industry 4.0 solutions. All these BP&I were jointly identified and validated by the project partners. The BP&I factsheets are published in a Knowledge Platform for Regional Forest Innovation, which is an open, multilingual repository (currently 13 European languages) created by the consortium to enable the widest possible dissemination of results. Spreading this knowledge in Europe will help practitioners and professionals to gain a better understanding of how the digital transformation in forestry can improve sustainable forest management and ecosystem resilience and thus benefit a more competitive forest-based sector in rural regions. The platform is accessible at: https://www.forestinnovationhubs.rosewood-network.eu

opencc-by-4.0May 2023View details →

Dataset - What are the Machine Learning best practices reported by practitioners on Stack Exchange?

The data correspond to the posts (questions and answers) retrieved by querying for posts related to the tag 'machine learning' and the phrase 'best practice(s).' The data were used as the basis for a study currently under review on discussing machine learning best practices as discussed by practitioners in question-and-answer communities such as Stack Exchange. The information from each type of post (i.e., questions and answers) is presented in multiple formats (i.e., .txt, .csv, and .xlsx).   Answers - Variables <ul> <li>AID:  Unique identification of the answer in the Q&A website.</li> <li>ParentId: Unique identification of the question associated with the answer in the Q&A website </li> <li>AcceptedAnswerId : In the case in which an answer is the most voted question associated with the ParentId, and it is different from the accepted answer, a different identifier from the AID is available. In the case in which the accepted question had a score lower than 1, a -1 is assigned. </li> <li>ABody:  HTML text of the answer.</li> <li>Score: Upvotes - downvotes of the answer.</li> <li>url_Answer:  URL of the answer. The question URL can be from different websites.  </li> <li>type: best or accepted. Accepted in the case that the information belongs to the accepted answer of the ParentId question and best in the case in which it is the most voted question of the ParentId question.</li> <li>Date: Creation date of the answer.</li> </ul> Questions - Variables <ul> <li>QID: Unique identification of the question in the Q&A website. </li> <li>AcceptedAnswerId: Unique identification of the accepted answer for a specific question in the Q&A website. In the case in which a question had a most-voted answer different from the accepted one, and the accepted one had a negative score, a -1 was assigned to the  AcceptedAnswerId. </li> <li>BestAnswerId: Unique identification of the most voted answer for a specific question in the Q&A website. In the case in which the most voted and accepted questions were the same, then a -1 was assigned to the BestAnswerId.  </li> <li>Qtitle: Title of the question.</li> <li>QBody: HTML text of the question.</li> <li>Score: Upvotes - downvotes of the questions.</li> <li>QTags: Tags that are associated with each question.</li> <li>url_question: URL of the question. The question URL can be from different websites.  </li> <li>Date: Creation date of the question</li> </ul> This dataset is a subset of the Stack Exchange dump of 03.2021 (<a href="https://archive.org/details/stackexchange_20210301">https://archive.org/details/stackexchange_20210301</a>) in which a series of filters were applied to obtain the data used in the study.

Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 mm Macro lens and processed with Agisoft Photoscan. The visual comparison of the mollusc shows a similar level of detail between photogrammetry and MechScan for the external surfaces, with still a bit more detail for the MechScan. The HDI Advance has a much lower resolution.

Fig. 5.6 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 5.6. Decimation of a 3D model. The four parts show a 3D model in various degrees of reducing the amount of faces. In the left upper corner is the original and rotating clockwise are the models at 50%, 75% and 90% decimation. Until 75% there is hardly any difference noticeable, while at 90% the cracks become less deep and the faces become more visible.

Fig. 6.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 6.2. Texture comparison between photogrammetry and MechScan (above) and an actual picture captured by a Canon 700D with 100 mm macro lens of the shell below.

Fig. 5.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 5.3. Micro-contrast enhancement in DxO OpticsPro 11. A crop of the original image is on the left, one of the post-processed pictures on the right.

Fig. 5.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 5.4. Micro-contrast enhancement in DxO OpticsPro 11. The original image is on the left, the postprocessed picture on the right. The post-processed picture looks more crisp and shows more details than the original one as the washed-out appearance has gone.

Fig. 6.14. Ishango rod. The left 3D model was acquired with a in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 6.14. Ishango rod. The left 3D model was acquired with a µCT many years ago. The middle one is scanned with the MechScan structured light scanner. The right one is the combination of both the µCT scan, the structured light scan and the texture of the photogrammetry model.

Fig. 5.1 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 5.1. Relighting in DxO OpticsPro 11. The original image is on the left, the post-processed picture on the right. The underexposed image is now corrected without the need to take new images.

Fig. 4.9 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 4.9. Pre-Columbian handle of an incense shovel from the Royal Museum of Art and History collections. UV fluorescence photogrammetry model. In this case, fluorescence enables to enhance the glue (fluorescing in green). https://sketchfab.com/models/2d82a98be64c48b89cada459b81bd0ab

Fig. 4.7 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 4.7. Enhancing the legibility of a specimen. The picture on the left represents the specimen captured under white light, while the picture on the right displays the specimen under UV light. Part of the reflections is reduced under UV light allowing to display more contrasted structures.

Fig. 4.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 4.5. Detail of the Halszkaraptor fossil from Mongolia. In white light on the left, in UV fluorescence on the right. The UV fluorescence image displays restorations of the fossils and treatment applied to preserve it.

Fig. 3.20. 3D in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.20. 3D model of a Dorylus ant (size: 1.5 cm) based upon focus stacked images, textured model is on the left, the view of only the mesh is on the right. The VCM option in Agisoft Photoscan is chosen to include small detail in the 3D model. The tibia spurs are clearly marked. https://sketchfab.com/models/da9aa414bfa64caabfe5c552368b16f0

Fig. 3.15 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.15. Part of the Cognisys StackShot 3X Deluxe Kit, reassembled for the photogrammetry purpose. The two rotary tables are mounted perpendicular to each other, whereby rotary table A moves a steel angle with rotary table B fixed at the end.

Fig. 3.13 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.13. Alnus japonica (2 cm long) scanned with DISC3D. A. EDOF-image. B. 3D-model (vcm) from 807 cameras. C. 3D-model from 398 cameras.