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141 results for “collecting practices”
Community Established Best Practice Recommendations for Tephra Studies-from Collection through Analysis
<p>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.</p> <p>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.</p> <p>For additional details, see the accompanying manuscript:</p> <p>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. <em>Sci Data</em> <strong>9, </strong>447 (2022). <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a></p> <p>*corresponding author: Kristi Wallace, <a href="mailto:kwallace@usgs.gov">kwallace@usgs.gov</a></p> <p>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>.</p>
Morphological and physical chemical characterization of main agricultural plastics articles used for protected cultivation systems during ageing in fields, and collection practices
<p>This dataset includes data generated upon the implementation of the ST 1.2.1 "Analysis of degradation and fragmentation of AP and transfer of MNP to soil". The activities dealt with the study of degradation and fragmentation from weathering and agricultural practices of conventional and biodegradable AP relevant for transfer of MNP to soil (during both use and end of life). In particular, the experimental data refer to characterization of biodegradable mulch films, pristine (coded M-BIO0) or subjected to photo-oxidative weathering (M-BIO192), as well as the same samples buried in soil for varying time periods, up to 353 days. The folders included contain gel permeation chromatography (GPC) and Matrix-assisted Laser Desorption Ionization (MALDI-TOF) data, which account for the change in film molecular weight upon soil burial. Furthermore, Differential Scanning Calorimetry (DSC) data and Scanning Electron Microscopy (SEM) and Water Contact Angle (WCA) images of some selected samples are also provided. The folder named MS RAW FILES.zip includes all the mass spectrometry raw data.</p>
MainstreamBIO_Collection of Nutrient Recycling Practices_Dataset1_2023.06.01_v1
<p>The dataset contains a developed set of Nutrient recycling practices (NRPs), which refer to the processes of recovering nutrients from organic waste and wastewater and returning them to agricultural land as recycled nutrient fertilizers (RNFs). In the processes, nutrients from nutrient-rich materials created during production and consumption are reused sustainably and safely as recycled nutrients. NRP is a sustainable practice that helps reduce waste and pollution while improving soil health and increasing crop yields. NRP also reduces the use of non-renewable natural resources and can reduce nutrient emissions into the environment.</p> <p>Most of the selected practices are widely used mainly in agriculture, but also in forestry. A large group of them is well recognized and accepted by stakeholders, but some of them are associated with a specific location where appropriate biomass and technology is available. A certain group of practices presents an innovative approach to the problem of nutrient recycling and these practices are not widely known and implemented.</p>
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.
Fig. 3.37 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.37. Excavation site scanned with the Gotcha infrared sensor. On the left is the site without texture, on the right with texture. The excavation site pictured measures approximately 4×4 m.
Fig. 3.10 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.10. Photogrammetry model of a Costa Rican Sacrificing Warrior (800–1300 AD) in Basalt (RMAH collections). The possibility of viewing the model without the texture has improved the visibility of the belt markings. https://sketchfab.com/models/03a9c7c61cdf48c8845498d1a6b19a73
Fig. 3.9 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.9. Example of photogrammetry model of Argonauta tuberculata https://sketchfab.com/models/daed659ee685452b91d8f8c91dff761b
Fig. 3.7 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.7. Example of a photogrammetry model of an archaeological copper necklace from DRCongo https://sketchfab.com/models/122d9a4660a24f5181bc586672c9ffe3
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.