Find research datasets worth reusing

Figure 3 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 3 - Example of identifying trees in a forest from LiDAR data. Illustrated is a small plot of poplar trees in Flevoland, The Netherlands, for which tree crowns and tree tops have been calculated.

Figure 2 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 2 - Generic workflow for object-based image analysis (OBIA) of LiDAR point clouds and proposed ecological applications. A workbench (blue) will be developed to handle the data storage, data exploration, and interactive OBIA of the massive LiDAR point clouds. Combined with datasets of bird distributions, climate, and other remote sensing layers (orange), the LiDAR data will be applied to several ecological case studies, e.g. by using species distribution modelling of birds and insect pollinators (green).

Figure 1 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 1 - The vertical and horizontal distribution of plants influences habitat structure and 3D characteristics of vegetation for animals. Illustrated are examples for (a) forests, (b) agricultural and open landscapes, and (c) reedbeds and marshlands. The height, openness and density of vegetation as well as specific habitat features (e.g. tree species, hedges etc.) are key aspects of animal habitat and space use.

Figure 2c from: Marek P (2017) Ultraviolet-induced fluorescent imaging for millipede taxonomy. Research Ideas and Outcomes 3: e14850. https://doi.org/10.3897/rio.3.e14850

Figure 2c - Foreleg of male Pseudopolydesmus canadensis, inset: striated muscles (top) and sphaerotrichomes (bottom)

Figure 1 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811

Figure 1 - Participants of the workshop on "Citizen Science and Open Data: a model for Invasive Alien Species in Europe". Image: COST Association.

Figure 4 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811

Figure 4 - Flipchart with participants notes addressing the topic "Main characteristics of a model for a citizen participation replicable across different policies" during Session 1. Image: COST Association.

Figure 3 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811

Figure 3 - Discussion of a round table through the "world café" method, addressing the topic "List of methods for mainstreaming inputs from CS in policy making including quality assurance and validations and other parameters" during Session 1. Image: COST Association.

Figure 2 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811

Figure 2 - Discussion of a round table through the "world café" method, addressing the topic "List of successful case-studies and examples of good practices in environment and IAS" during Session 1. Image: COST Association.

Figure 1 from: Woolfrey L (2017) Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa. Research Ideas and Outcomes 3: e14837. https://doi.org/10.3897/rio.3.e14837

Figure 1 - Tobacco Data in Africa Project Data Inventory 2016. Original data available as Suppl. material 1.

Figure 2 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e14673. https://doi.org/10.3897/rio.3.e14673

Figure 2 - The core issues and principles that a Research Data Management policy should address, adapted from Hodson and Molloy 2015

Figure 1 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e14673. https://doi.org/10.3897/rio.3.e14673

Figure 1 - Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used.

Figure 4 from: Bingham H, Doudin M, Weatherdon L, Despot-Belmonte K, Wetzel F, Groom Q, Lewis E, Regan E, Appeltans W, Güntsch A, Mergen P, Agosti D, Penev L, Hoffmann A, Saarenmaa H, Geller G, Kim K, Kim H, Archambeau A, Häuser C, Schmeller D, Geijzendorffer I, García Camacho A, Guerra C, Robertson T, Runnel V, Valland N, Martin C (2017) The Biodiversity Informatics Landscape: Elements, Connections and Opportunities. Research Ideas and Outcomes 3: e14059. https://doi.org/10.3897/rio.3.e14059

Figure 4 - The network of biodiversity informatics organisations. The network visualisation was created using NodeXL (Version 1.0.1.229) (Smith et al. 2009) and was laid out with the Harel–Koren Fast Multiscale algorithm and then adjusted manually to remove overlaps. The colours represent clusters identified using the Girvan–Newman algorithm.

Figure 3 from: Bingham H, Doudin M, Weatherdon L, Despot-Belmonte K, Wetzel F, Groom Q, Lewis E, Regan E, Appeltans W, Güntsch A, Mergen P, Agosti D, Penev L, Hoffmann A, Saarenmaa H, Geller G, Kim K, Kim H, Archambeau A, Häuser C, Schmeller D, Geijzendorffer I, García Camacho A, Guerra C, Robertson T, Runnel V, Valland N, Martin C (2017) The Biodiversity Informatics Landscape: Elements, Connections and Opportunities. Research Ideas and Outcomes 3: e14059. https://doi.org/10.3897/rio.3.e14059

Figure 3 - An example of links from the biodiversity informatics landscape to the policy-level: the Biodiversity Indicators Partnership (BIP).

Figure 2 from: Bingham H, Doudin M, Weatherdon L, Despot-Belmonte K, Wetzel F, Groom Q, Lewis E, Regan E, Appeltans W, Güntsch A, Mergen P, Agosti D, Penev L, Hoffmann A, Saarenmaa H, Geller G, Kim K, Kim H, Archambeau A, Häuser C, Schmeller D, Geijzendorffer I, García Camacho A, Guerra C, Robertson T, Runnel V, Valland N, Martin C (2017) The Biodiversity Informatics Landscape: Elements, Connections and Opportunities. Research Ideas and Outcomes 3: e14059. https://doi.org/10.3897/rio.3.e14059

Figure 2 - A highly-connected element in the landscape: the Global Biodiversity Information Facility (GBIF).

Figure 1 from: Despot-Belmonte K, Doudin M, Groom Q, Wetzel F, Agosti D, Jacobsen K, Smirnova L, Weatherdon L, Robertson T, Penev L, Regan E, Hoffmann A, MacSharry B, Shennan-Farpon Y, Martin C (2017) EU BON's contributions towards meeting Aichi Biodiversity Target 19. Research Ideas and Outcomes 3: e14013. https://doi.org/10.3897/rio.3.e14013

Figure 1 - This infographic illustrates EU BON's contribution towards meeting Aichi Biodiversity Target 19.

Figure 1 from: Schoening T, Durden J, Preuss I, Branzan Albu A, Purser A, De Smet B, Dominguez-Carrió C, Yesson C, de Jonge D, Lindsay D, Schulz J, Möller K, Beisiegel K, Kuhnz L, Hoeberechts M, Piechaud N, Sharuga S, Treibitz T (2017) Report on the Marine Imaging Workshop 2017. Research Ideas and Outcomes 3: e13820. https://doi.org/10.3897/rio.3.e13820

Figure 1 - Participants of the Marine Imaging Workshop 2017 in the Lithothek of GEOMAR (Photo: Jan Steffen, GEOMAR).

Figure 6 from: Mounce R, Murray-Rust P, Wills M (2017) A machine-compiled microbial supertree from figure-mining thousands of papers. Research Ideas and Outcomes 3: e13589. https://doi.org/10.3897/rio.3.e13589

Figure 6 - The consensus supertree produced from an analysis of 924 source trees from the journal IJSEM.

Figure 1 from: Mounce R, Murray-Rust P, Wills M (2017) A machine-compiled microbial supertree from figure-mining thousands of papers. Research Ideas and Outcomes 3: e13589. https://doi.org/10.3897/rio.3.e13589

Figure 1 - Overall workflow; from content acquisition to stripping figure images out of the PDF, to image filtering, image analysis and reconversion back into re-usable, machine-readable phylogenetic data.

Figure 4 from: Mounce R, Murray-Rust P, Wills M (2017) A machine-compiled microbial supertree from figure-mining thousands of papers. Research Ideas and Outcomes 3: e13589. https://doi.org/10.3897/rio.3.e13589

Figure 4 - Output from image analysis of the input tree image in figure 1. All taxa and relationships are correctly reproduced, with branch lengths also preserved with high fidelity. (Note that the vertical ordering of the tips is not meaningful and is arbitrarily created by the display software.)

Figure 5 from: Mounce R, Murray-Rust P, Wills M (2017) A machine-compiled microbial supertree from figure-mining thousands of papers. Research Ideas and Outcomes 3: e13589. https://doi.org/10.3897/rio.3.e13589

Figure 5 - Screenshot of exemplar machine-readable NeXML formatted data output from our automated analysis of the figure image from figure 1 of Park et al. 2008. Note that the genus, species, strain, and Genbank Accession numbers are semantically distinguished where detected. Heuristic post-OCR autocorrection processes are also noted where these have been applied (e.g. the conversion of a letter 'Z' to the number '2' in many Genbank Accession numbers). A machine-readable version of this file is supplied as supplementary material (Suppl. material 2).