Find research datasets worth reusing

Figure 3 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 3 - Number of leaves (terminal taxa) in each of 1614 source tree images (blue) and number of leaves recovered-from each image (orange). The modal number of taxa recovered per image was 12, the median was 13, and the mean was 13.96. The modal number of taxa not recovered from the trees was 2, the median was 5 and the mean was 7.15. The image mining process is lossy since most output tree files did not recover all of the taxa from the source image.

Figure 2 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 2 - A typical source input tree raster image (figure 1 from Park et al. 2008). Note the low resolution image quality. As this computer-generated ilustration follows predefined rules and conventions for the visual display of phylogenetic trees, we do not believe that it qualifies as a copyrightable work in itself (see Egloff et al. 2017 for more).

Figure 7 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 7 - Comparison between our supertree (left) and the NCBI Taxonomy reference tree (right): This example section of the supertree corresponds to taxa mostly from Rhodospirillaceae with the exception of rogue taxa indicated with a red asterisk. This section is related to the NCBI taxonomy reference tree on the right, containing those Rhodospirillaceae species leaves included in the supertree analysis (27). Nine taxa out of the 27 Rhodospirillaceae included were reconstructed elsewhere in our supertree (not shown). This is representative of the phylogenetic placement errors found throughout the supertree: individual rogue taxa, as well as misplaced clades of related taxa.

Figure 8 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 8 - A visual exploration of taxon overlap of the 924 source trees used in this supertree analysis using the Supertree Toolkit 2 (Hill and Davis 2014). This demonstrates that there is not connectivity between all of the source trees we used in our supertree analysis.

Figure 1 from: Vanderhoeven S, Adriaens T, Desmet P, Strubbe D, Backeljau T, Barbier Y, Brosens D, Cigar J, Coupremanne M, De Troch R, Eggermont H, Heughebaert A, Hostens K, Huybrechts P, Jacquemart A, Lens L, Monty A, Paquet J, Prévot C, Robertson T, Termonia P, Van De Kerchove R, Van Hoey G, Van Schaeybroeck B, Vercayie D, Verleye T, Welby S, Groom Q (2017) Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes 3: e13414. https://doi.org/10.3897/rio.3.e13414

Figure 1 - A visual description of the TrIAS workflow through work packages. Work package 1 generates the input data; Work package 2 creates indicators and summaries of the data; Work package 3 uses the data and generates models and predications of future distributions; Work package 4 involves experts using the information from the other work packages, together with their own experience to create impact assessments.

Figure 3 from: Bethlehem R, Falkiewicz M, Freyberg J, Parsons O, Farahibozorg S, Pretzsch C, Soergel B, Margulies D (2017) Gradients of cortical hierarchy in Autism. Research Ideas and Outcomes 3: e13391. https://doi.org/10.3897/rio.3.e13391

Figure 3 - Average z-scores obtained from unthresholded reverse-inference neurosynth meta-analysis activation maps. We divided the principle gradient into percentiles and used this to create a mask. We then calculated the average z-score inside this mask on neurosynth maps.

Figure 2 from: Bethlehem R, Falkiewicz M, Freyberg J, Parsons O, Farahibozorg S, Pretzsch C, Soergel B, Margulies D (2017) Gradients of cortical hierarchy in Autism. Research Ideas and Outcomes 3: e13391. https://doi.org/10.3897/rio.3.e13391

Figure 2 - Goodness of fit for principal gradient. Ranked according to the median goodness of fit for both groups.

Figure 1 from: Bethlehem R, Falkiewicz M, Freyberg J, Parsons O, Farahibozorg S, Pretzsch C, Soergel B, Margulies D (2017) Gradients of cortical hierarchy in Autism. Research Ideas and Outcomes 3: e13391. https://doi.org/10.3897/rio.3.e13391

Figure 1 - Linear fit of gradient slopes for the top 10 gradients for each group (1 == neurotypical individuals; 2 == individuals with autism).

Figure 9 from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 9 - Graphical representation of neurobiological requirements for Standard Raven's Progressive Matrices questions. The NOB score (bottom) and the PCT score (top) as a function of question number. The five sets of 12 questions are shown with different markers.

Figure 8 from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 8 - Graphical representation of neurobiological requirements for TONI-4, Form B. The NOB score (bottom) and the PCT score (top) as a function of question number.

Figure 7 from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 7 - Graphical representation of neurobiological requirements for TONI-4, Form A. The minimal number of objects involved in mental calculations (the NOB score, bottom) and the minimal amount of posterior cortex territory required (the PCT score, top) as a function of question number.

Figure 5c from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 5c - shows a question in which mental synthesis of two objects has to be conducted according to the following rule specified in the top row: "the object in the middle column goes on top of the object in the left column" (the solution in the second square).

Figure 1 from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 1 - Visual information processing in the cortex. From the primary visual cortex (V1, shown in yellow), the visual information is passed in two streams. The neurons along the ventral stream also known as the ventral visual cortex (shown in purple) are primarily concerned with what the object is. The ventral visual stream runs into the inferior temporal lobe. The neurons along the dorsal stream also known the dorsal visual cortex (shown in green) are primarily concerned with where the object is. The dorsal visual stream runs into the parietal lobe.

Figure 2b from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 2b - A more complicated object, where the top row of the matrix indicates the rule: "the object in the right column is the same as the object in the left column" (the 6th square). The "Find the same object" questions were assigned the NOB score of one and the PCT score of zero. Note: since all three IQ tests investigated in this report are copyrighted, the examples presented are not actual items from a test but are representative of a typical question.

Figure 5a from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 5a - requires the combination of two objects. The top two rows of the matrix indicate the rule: "the object in the right column is the result of the combination of the two objects shown in the left and middle row" (the solution in the 5th square).

Figure 3c from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 3c - Integration of number modifier. The top two rows of the matrix indicate the rule: "the object in the right column is the result of combining number indicated in the column and the object indicated in the row" (solution: the three squares in the fifth cell). Since integration of modifiers involves modification of neurons encoding a single object, this type of questions was assigned the NOB score of one. Integration of size and color modifier questions were assigned a PCT score of one since modification is limited to the ventral visual cortex (Gabay et al. 2016). Integration of number modifier questions were assigned a score of two since the numerical information is represented by regions of the posterior parietal lobes (Dehaene et al. 2004, Nieder and Dehaene 2009).

Figure 1 from: Jay C, Haines R, Vigo M, Matentzoglu N, Stevens R, Boyle J, Davies A, Del Vescovo C, Gruel N, Le Blanc A, Mawdsley D, Mellor D, Mikroyannidi E, Rollins R, Rowley A, Vega J (2017) Identifying the challenges of code/theory translation: report from the Code/Theory 2017 workshop. Research Ideas and Outcomes 3: e13236. https://doi.org/10.3897/rio.3.e13236

Figure 1 - In many areas of research, science is now produced at the intersection of the 'software', contributed by programmers, and the 'story', or theoretical narrative, contributed by domain experts. In team-based research, everyone is a scientist.

Figure 2a from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 2a - A typical 2x2 matrix commonly used in an IQ test, with six answer choices displayed below the problem. The top row of the matrix indicates the rule: "the object in the right column is the same as the object in the left column." Applying this rule to the bottom row, we arrive at the correct answer: the white rhombus.

Figure 3b from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 3b - Integration of size modifier. The top two rows of the matrix indicate the rule: "the object in the right column is the result of combining size indicated in the column and the object indicated in the row" (solution: the square in the fifth cell).

Figure 12 from: Vyshedskiy A, Dunn R, Piryatinsky I (2017) Neurobiological mechanisms for nonverbal IQ tests: implications for instruction of nonverbal children with autism. Research Ideas and Outcomes 3: e13239. https://doi.org/10.3897/rio.3.e13239

Figure 12 - Graphical representation of neurobiological requirements for questions WISC-V, Figure Weights. The NOB score (bottom) and the PCT score (top) as a function of question number.