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Figure 7 from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 7 Existing services forming the nucleus of the envisioned federation of repositories as part of the NFDI4Chem infrastructure.
Figure 11 from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 11 TA6 will manage the synergies and interfaces to other NFDI consortia and the NFDI as a whole. It will ensure that cross-cutting topics such as NFDI-wide services are properly addressed and their results incorporated into NFDI4Chem.
Figure 10 from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 10 The cultural change in chemical RDM is supported through training and community involvement in TA5 as well as by the NFDI4Chem infrastructure.
Figure 1d from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1d A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Fossilised animal skin (Natural History Museum 2009)
Figure 1b from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1b A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Pinned insect specimen (Natural History Museum 2018)
Figure 1c from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1c A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Microscope slide (Natural History Museum 2017)
Figure 1a from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1a A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Herbarium specimen (Natural History Museum 2007a)
Figure 11 from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 11 The distribution of languages across the specimen and herbaria. EN=English, FR=French, LA=Latin, ET=Estonian, DE=German, NL=Dutch, PT=Portuguese, ES=Spanish, SV=Swedish, RU=Russian, FI=Finnish, IT=Italian, ZZ=Unknown. The codes for the contributing herbaria are listed in Table 11 (from Dillen et al. 2019).
Supplementary material 1 from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Appendices
Supplementary material 1 from: Cocks N, Livermore L, Smith VS, Woodburn M (2020) Technical capacities of digitisation centres within ICEDIG participating institutions. Research Ideas and Outcomes 6: e55522. https://doi.org/10.3897/rio.6.e55522
Survey Template
Figure 1e from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1e A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Liquid preserved specimen (Natural History Museum 2010)
Figure 7 from: Walton S, Livermore L, Dillen M, De Smedt S, Groom Q, Koivunen A, Phillips S (2020) A cost analysis of transcription systems. Research Ideas and Outcomes 6: e56211. https://doi.org/10.3897/rio.6.e56211
Figure 7 Distribution of the time to transcribe a single herbarium sheet. All times greater than 1000 seconds have been aggregated in a single bar. Graph generated in R 3.6.1.
Figure 6 from: Altenhöner R, Blümel I, Boehm F, Bove J, Bicher K, Bracht C, Brand O, Dieckmann L, Effinger M, Hagener M, Hammes A, Heller L, Kailus A, Kohle H, Ludwig J, Münzmay A, Pittroff S, Razum M, Röwenstrunk D, Sack H, Simon H, Schmidt D, Schrade T, Walzel A-V, Wiermann B (2020) NFDI4Culture - Consortium for research data on material and immaterial cultural heritage. Research Ideas and Outcomes 6: e57036. https://doi.org/10.3897/rio.6.e57036
Figure 6 Multimodal data types. Example 1: Motion Bank, Example 2: Digital Mozart Score Viewer, Example 3: Inscriptions in their Spatial Context (IBR).
Figure 8 from: Sugiharto SP, Bintoro N, Karyadi JNW, Pranoto Y (2020) Supercritical carbon dioxide pasteurization to reduce the activity of muscle protease and its impact on physicochemical properties of Nile tilapia. Research Ideas and Outcomes 6: e56887. https://doi.org/10.3897/rio.6.e56887
Figure 8 Effect of HPCD (40 °C and 15 min) of hardness of fillet. Bars with the same letter were not significantly different. Vertical lines indicate standard deviation.
Figure 2 from: Sugiharto SP, Bintoro N, Karyadi JNW, Pranoto Y (2020) Supercritical carbon dioxide pasteurization to reduce the activity of muscle protease and its impact on physicochemical properties of Nile tilapia. Research Ideas and Outcomes 6: e56887. https://doi.org/10.3897/rio.6.e56887
Figure 2 The appearance of whole fish Nile tilapia after subjected to CO2 pasteurization at 40 oC and for 15 min with the following pressures: A. No treatment, B. 70 bar, C. 75 bar, D. 80 bar, E. 85 bar, and F. 90 bar
Supplementary material 1 from: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602
Tools and services evaluation speadsheet
Figure 1 from: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602
Figure 1 An overview of potential Specimen Data Refinery workflows based on image inputs and their derivatives, datasets and services.
Figure 2 from: Willemse L, Runnel V, Saarenmaa H, Casino A, Gödderz K (2020) Digitisation of private collections. Research Ideas and Outcomes 6: e57767. https://doi.org/10.3897/rio.6.e57767
Figure 2 Lower part of the "Field Trip Report" form on www.laji.fi, where observations and their details can be entered.
Figure 1 from: Willemse L, Runnel V, Saarenmaa H, Casino A, Gödderz K (2020) Digitisation of private collections. Research Ideas and Outcomes 6: e57767. https://doi.org/10.3897/rio.6.e57767
Figure 1 Upper part of the "Field Trip Report" form on the Notebook Service of the FinBIF portal (www.laji.fi), where details of the gathering event can be entered.
Supplementary material 4 from: Dixey K, Woodburn M, Hardy H, Livermore L, Smith VS (2020) Identification of provisional Centres of Excellence for digitisation of European natural science collections. Research Ideas and Outcomes 6: e57750. https://doi.org/10.3897/rio.6.e57750
WP7 MS45 Centres of Excellence - Service Descriptions
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.