Find research datasets worth reusing

Fig. 2 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)

Fig. 2. Maximum Likelihood tree of the Oligotrichida inferred from small subunit ribosomal RNA (SSU rRNA) gene sequences (66 taxa and 1823 nucleotide positions) aligned with the Muscle algorithm (Edgar 2004) implemented in MEGA ver. 5.1 (Tamura et al. 2011). The alignment is available upon request. The tree was computed with RAxML (Stamatakis et al. 2008) and the datasets were bootstrap re-sampled 100 times. Support values are listed at the nodes. The second values at the nodes represent the posterior probability values of a Bayesian Inference analysis performed with MrBayes (Ronquist and Huelsenbeck 2003). Values below 50% and 0.5, respectively, are represented by a dash. * – initially published as Spirostrombidium sp.; ** – initially published as Parallelostrombidium sp.

Fig. 4 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)

Fig. 4. Evolution of kinetid structures in the somatic ciliature of choreotrichid ciliates. The aloricate taxa have only one kinetid type, except for Leegaardiella elbraechteri and Lynnella. Tintinnids with ventral organelles have two (Tintinnidium, subgenus Tintinnidium), rarely one (Tintinnopsis cylindrata, Membranicola) or three (Tintinnidium, subgenus Semitintinnidium) kinetid types. Extant tintinnids with a ventral kinety have some dikinetids with two cilia and many monokinetids or some dikinetids with two cilia, some dikinetids with one cilium, and many monokinetids.

Fig. 1 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)

Fig. 1. Hypothetical evolution of oligotrichid somatic ciliary patterns (0–IV, VI, VII, after Agatha 2011b; V, VIII–XIV, originals; protargol impregnation). Small arrows mark orientation of kineties (posterior to anterior). Arrowheads denote dorsal breaks in girdle kinety. Dotted arrows mark the tontoniid evolution. Dotted circles denote position of oral primordium in early dividers. Type 0 – dorsal kineties of hypotrich-like ancestor; Type I – strombidiid Parallelostrombidium; Type II – strombidiid Novistrombidium and tontoniid Tontonia; Type III – strombidiid Spirostrombidium; Type IV – strombidiid Omegastrombidium; Type V – strombidiid Strombidium, pelagostrombidiid Limnostrombidium, and tontoniid Pseudotontonia; Type VI – tontoniid Paratontonia; Type VII – tontoniids Laboea and Spirotontonia; Type VIII – strombidiid Foissneridium; Type IX – strombidiid Opisthostrombidium; Type X – cyrtostrombidiid Cyrtostrombidium; Type XI – strombi- diid Williophrya; Type XII – strombidiid Apostrombidium; Type XIII – hypothetic stage; Type XIV – strombidiid Varistrombidium. EX – extrusome attachment sites, GK – girdle kinety, OP – oral primordium, VK – ventral kinety.

Fig. 3 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)

Fig. 3. Maximum Likelihood tree of the Choreotrichida inferred from small subunit ribosomal RNA (SSU rRNA) gene sequences (138 taxa and 1859 nucleotide positions) aligned with the Muscle algorithm (Edgar 2004) implemented in MEGA ver. 5.1 (Tamura et al. 2011). The alignment is available upon request. The tree was computed with RAxML (Stamatakis et al. 2008) and the datasets were bootstrap re-sampled 100 times. Support values are listed at the nodes. The second values at the nodes represent the posterior probability values of a Bayesian Inference analysis performed with MrBayes (Ronquist and Huelsenbeck 2003). Values below 50% and 0.5, respectively, are represented by dashes. Branches with unambiguously clustered taxa are collapsed, species of the genus Tintinnopsis grouped in 5 different clades numbered I–V. Most common lorica structures: – hyaline; – entirely agglomerated; – composed of hyaline collar and agglomerated bowl; * – after Kofoid and Campbell (1929) a synonym of Codonella cratera; ** – does not correspond with the redescription of Agatha and Riedel-Lorjé (2006); *** – possibly incorrectly identified, might be Dadayiella acutiformis; **** – invalid taxon, very likely a replacement lorica (see text).

opencc-by-4.0Aug 2023View details →

What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica (Interpolated Data)

This dataset accompanies the paper 'What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica' in Journal of Glaciology, and can be used alongside the code found on Github (https://github.com/rohaizharis/inversion_radar2022) to reproduce the figures. The dataset consists of ice-penetrating radar data (specularity and relative reflectivity) and basal shear stress inversions that have been linearly interpolated onto radar flight tracks.

opencc-by-nc-sa-4.0Aug 2024View details →

Supplementary material for the article "Reaching Meaning through Language: What can Children Tell Us about Distributivity?"

<div> <div>This data set includes the supplementary material for the article "Reaching Meaning through Language: What can Children Tell Us about Distributivity?". All content is documented in the README.md file.</div> <div>Abstract: Sentences with a plural subject receive a distributive reading if the predicate refers to the atomic members or a collective one if it relates to the whole group. Previous accounts suggest that the distributive representation includes an additional semantic operator, and comprehension experiments show that adults interpret an ambiguous sentence as collective. However, children accept distributive readings more often, questioning their presumed greater difficulty. The current study investigates these interpretations in a novel way through a production study. Italian adults and preschoolers described distributive and collective pictures. We found that adults produced more distributive expressions, in line with semantic theories and psycholinguistic findings. Children were not fully sensitive to the need to express markers disambiguating the two readings. However, when they recognised the difference between pictures, they produced more collective markers, different from adults. We discussed our results at the intersection of language acquisition, semantic theories, and cognitive development.</div> </div>

opencc-by-4.0Jan 2022View details →

TELL me what you See (TESE)

Dataset reported in the deliverable D3.10 Description TEll me what you SEe (TESE) dataset is a gaze annotated dataset, tailored for training deep learning architectures and extracting useful viewing patterns. It was created following the “description” paradigm where subjects were asked to orally describe visual scenes. Fifty-six different subjects participated in the recording procedure using Tobbi Pro eye tracker sensor. The publicly available Visual Genome (VG) dataset is a generic content image database with object bounding box, class and relation dense annotations. Content The gaze annotations are provided with the JSON file format using the following naming convention. imageID.json The imageID denotes the Visual Genome image id which is actually the filename of the image. Inside the JSON file gaze points annotations, timestamp and x,y coordinates and transcribed text from voice description are included as presented below. {“subjectID”:1, “gaze”:[{“Time”: 6105.218916422819, “X”: 0.5127838850021362, “Y”: 0.4649979770183563}, {“Time”: 6105.2274152988775, “X”: 0.507378339767456, “Y”: 0.4624013900756836}] } In addition, the subjects.json file provides information about the subjects as described below. [{“id”: “1”, “gender”: “Male”, “age”: “38”}, {“id”: “2”, “gender”: “Female”, “age”: “29”}]

opencc-by-4.0Mar 2022View details →

TELL sample output data

This dataset contains sample output data for TELL. The sample dataset includes four years of sample future data (2039, 2059, 2079, and 2099) that comes from IM3's future WRF runs under the RCP 8.5 climate scenario with SSP5 population forcing. Note that the GCAM-USA output used in this simulation is sample data only. As such the quantitative results from this set of sample output should not be considered valid.

opencc-by-4.0Sep 2021View details →

Supporting data for the Total ELectricity Loads (TELL) model

This is the raw data supporting the Total ELectricity Loads (TELL) model developed by the Integrated Multisector Multiscale Modeling (IM3) project led by Pacific Northwest National Laboratory. Information about the model itself and code to extract and work with this raw data can be found at: https://github.com/IMMM-SFA/tell. There are five core datasets in this package: 1) EIA_930 contains data on hourly electricity demand for 68 balancing authorities in the U.S. 2) EIA_861 contains an annual summary of the characteristics of the power industry in the U.S. 3) Population contains annual estimates of county- and state-level population in the U.S. 4) County_Shapefiles contains a set of shapefiles that define county boundaries for counties in the U.S. 4) State_Shapefiles contains a set of shapefiles that define state boundaries for states in the U.S.

opencc-by-4.0Jun 2022View details →

Science communication: How to tell the story of your scientific work

Have you ever wondered why certain research projects get picked up in the news and not others? Or how some researchers manage to produce science content that goes viral on social media? Sure, part of it is luck, but another part of it is storytelling. By framing your research in a different way, you can increase the chances that your story gets picked up, or that your social media gains a following. <a href="https://www.youtube.com/watch?v=aasLG7uOGAg">This webinar </a>will give you the tools to tell stories about your research intentionally, identifying newsworthy stories, who’s your audience, what medium best fits your story, and considering whether you want to pitch your story to journalists, or perhaps use your own media production skills, and posting it to social media. But what platform? We will cover all of this and more in part 1 of our Arctic PASSION seminar! This is part 1 of a series of seminars that Arctic PASSION will be hosting. Arctic PASSION is an EU Horizon 2020-funded project which aims to build a coherent Arctic Observing System that is adjusted to societal needs based on a co-design of knowledge. The Arctic PASSION Online Seminar and Dialogue Series is a tool to communicate project’s topics, share ideas, plans and results, and initiate an inclusive and proactive dialogue with people from different groups, backgrounds and career levels. It is targeted to Arctic and Indigenous Youth, Early Career Scientists and other interested audiences. The online seminar is led by Olivia Rempel, a documentary filmmaker and multimedia journalist working at GRID-Arendal, where she does everything from producing, shooting and editing documentaries, to guest teaching a mini science communication course at the Technical University of Denmark. She holds a master’s degree from the UC Berkeley Graduate School of Journalism, with prior undergraduate work in both journalism and environmental studies. Olivia has had a variety of media jobs, from logistics and communication work at Students on Ice, an educational polar expedition organization, to leading open-source investigations that combat disinformation at the UC Berkeley Human Rights Center and working on documentaries that have been screened at film festivals from Svalbard to Addis Ababa. Olivia has been working alongside passionate researchers for much of her career, and one of her greatest joys is helping them ensure their important work is communicated accurately and effectively. Useful Links: Watch this video on Youtube: <a href="https://www.youtube.com/watch?v=aasLG7uOGAg Olivia's public profile and contact details: https://www.grida.no/staff/108">https://www.youtube.com/watch?v=aasLG7uOGAg </a> Olivia's public profile and contact details: <a href="https://www.grida.no/staff/108">https://www.grida.no/staff/108</a> Olivia's slides: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbE13RnFpMDZiMlFMSjlOLV9oMVRfWjFrdm5Zd3xBQ3Jtc0ttWlJVZzVPMGE1djNsTGgwcmMweUNpeUkwQVhCT1FSdy1wVEFTRjdlRVhRVW41dkZmODZWbUNLc1VBeVhfNW9lYnRqTTdJOEdhc1hBUzQtV0dvcUpDQjNiYzRUV0NpSDZ3UzRrRl9mSl90c3Z3cW9hVQ&q=https%3A%2F%2Fnextcloud.awi.de%2Fs%2F2Gnj8pprcia9mD6&v=aasLG7uOGAg">https://nextcloud.awi.de/s/2Gnj8pprci...</a> List of databases mentioned: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVNenZNcEFLV01ielJrdFVQa3hkZlpDMnYyQXxBQ3Jtc0tsNXZOT2h3VHJtNTZ0akVtdzVLNFl3Smd1dmdvN1RVSnB6VXRacHFuSGZFM3hJUGtMOHI3UXpEaXhPWnRoOGZnOFYxYXdoTXdwN2JaSTBLb0Z5bERfczh1VEFiWUFfVDFQaDdBRktPT1I1cDdpVnVLRQ&q=https%3A%2F%2Fresearchguides.journalism.cuny.edu%2Ffindingexperts%2Fdiverse-experts&v=aasLG7uOGAg">https://researchguides.journalism.cun...</a> GRID-Arendal media resources, free for reuse: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0V3WVA2bUhHeVp5dUVLQUxRaW5pYU9UUm5jQXxBQ3Jtc0trYXBsUExha2d3QmMydzhDOEtaUjJnZU1DMElrdFE3QXV2Rmo5d0NSRXh6UWdhLTVjdC1XT0E3VkhIQUx0c193cjcwd0w4NEo4cnBFTzhfY19DYXlRR3FJTzd3VWtRZEl6dHFTOWJiVk9jekRYX1c5Yw&q=https%3A%2F%2Fwww.grida.no%2Fresources&v=aasLG7uOGAg">https://www.grida.no/resources</a> Science communication citations: Bickford D, Posa MRC, Qie L, Campos-Arceiz A, Kudavidanage EP. Science communication for biodiversity conservation Biological conservation.. 2012 Jul;151(1):74-76. DOI: 10.1016/j.biocon.2011.12.016. Bullock OM, Shulman HC and Huskey R (2021) Narratives are Persuasive Because They are Easier to Understand: Examining Processing Fluency as a Mechanism of Narrative Persuasion. Front. Commun. 6:719615. doi: 10.3389/fcomm.2021.719615 Márquez MC and Porras AM (2020) Science Communication in Multiple Languages Is Critical to Its Effectiveness. Front. Commun. <a href="https://www.youtube.com/watch?v=aasLG7uOGAg&t=331s">5:31</a>. doi: 10.3389/fcomm.2020.00031 Pavelle S and Wilkinson C (2020) Into the Digital Wild: Utilizing Twitter, Instagram, YouTube, and Facebook for Effective Science and Environmental Communication. Front. Commun. 5:575122. doi: 10.3389/fcomm.2020.575122

opencc-by-4.0Dec 2021View details →

Ancillary data for the wrf_to_tell processing chain

This data supports the sequence of processing scripts that convert the meteorology from IM3's climate simulations using the Weather Research and Forecasting (WRF) model into input files ready for use in the Total ELectricity Load (TELL) model. More details about the processing chain and the associated scripts can be found in the IM3 components library: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.

FIGURE 6 in A feeding organ the basihyal and thyrohyal tells which size of prey do true baleen whales (Cetacea, Chaeomysticeti) eat

FIGURE 6. Morphological traits of the basihyal and thyrohyal among the Chaeomysticeti separated by prey types. Boxes gray in colour are extinct baleen whales, which ID number 4, 8, 11, 33, 40, 53, 61, 70 in Table 2 are used here. Piscobalaena nana shows two different types of phylogenetic hypotheses (see in cladogram section).

FIGURE 5 in A feeding organ the basihyal and thyrohyal tells which size of prey do true baleen whales (Cetacea, Chaeomysticeti) eat

FIGURE 5. Same results as in Figure 4 with 90% confidence intervals for combinations of prey capture tactics and prey types. Numbers and letters are IDs and abbreviations of scientific names (see Table 2).

FIGURE 4 in A feeding organ the basihyal and thyrohyal tells which size of prey do true baleen whales (Cetacea, Chaeomysticeti) eat

FIGURE 4. The results of principal component analysis. Ovals represent 90% confidence intervals for prey types of the extant taxa. Diagrams of the shape changes in the positive directions are given along each axis. Numbers and letters are IDs and abbreviations of scientific names (see Table 2).

FIGURE 3 in A feeding organ the basihyal and thyrohyal tells which size of prey do true baleen whales (Cetacea, Chaeomysticeti) eat

FIGURE 3. Outlines of analyzed true baleen whale specimens. Numbers are given in Table 1 and Appendix 1. Abbreviations mean prey capture tactics (Sk: Skim, Mu: Multiple, Lu: Lunge) and prey types (Sm: Small, Bo: both large and small prey, La: Large, Un: Unknown).

FIGURE 2 in A feeding organ the basihyal and thyrohyal tells which size of prey do true baleen whales (Cetacea, Chaeomysticeti) eat

FIGURE 2. Example semi-landmark in the ventral view of the basihyal and thyrohyal with anatomical terms. The one of Balaenoptera musculus number 66 in Table 1 is used. The origins for the muscles were modified from Schulte (1916) with minor modification following Reidenberg and Laitman (1994) on the omohyoid muscle insertion.

FIGURE 1. A in A feeding organ the basihyal and thyrohyal tells which size of prey do true baleen whales (Cetacea, Chaeomysticeti) eat

FIGURE 1. A. Prey size of baleen whales modified from Gaskin (1982) with prey information in Jefferson et al. (2008). B. Modern baleen whale phylogeny and information of prey types + prey capture tactics. Phylogeny was combined the tree of the Balaenopteridae in Rosel et al. (2021) and the tree in Steeman et al. (2009) for the relationships of others.

Figure 1 in Can wildlife mortality on a local road tell something general? An answer from a protected area in south-western Romania

Figure 1. Study area (blue line–rivers, black line–roads, discontinuous line–Iron Gates Natural Park limits, black dots–localities, red dots–the six studied sectors on the road to Bigăr).