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Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".
<p>This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper:</p> <p>Ponti, M., Seredko, A. <a href="http://doi.org/10.1057/s41599-022-01049-z">Human-machine-learning integration and task allocation in citizen science.</a> <em>Humanit Soc Sci Commun</em> <strong>9, </strong>48 (2022). https://doi.org/10.1057/s41599-022-01049-z</p> <p>The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.</p> <p> </p> <p> </p>
Analytic dataset informing modeling of winter species distributions of North American bat species
<p>The fungal pathogen <i>Pseudogymnoascus destructans</i> and resultant white-nose syndrome (WNS) continues to advance across North America, infecting new bat populations, species, and hibernacula. Western North America hosts the highest bat diversity in the U.S. and Canada, yet little is known about hibernacula and hibernation behavior in this region. An improved understanding of where bats hibernate and the conditions that create suitable hibernacula is critical if land managers are to anticipate and address the conservation needs of WNS-susceptible species in regions yet to be infected. We estimated suitability of potential winter hibernaculum sites across the ranges of five bat species occurring in western North America. We estimated winter survival capacity from a mechanistic survivorship model based on bat bioenergetics and climate conditions. Leveraging the Google Earth Engine platform for spatial data processing, we used boosted regression trees to relate these estimates, along with key landscape attributes, to bat occurrence data in a hybrid correlative-mechanistic approach. Winter survival capacity, topography, land cover, and access to caves and mines were important predictors of winter hibernaculum selection, but the shape and relative importance of these relationships varied among species. This suggests that the occurrence of bat hibernacula can, in part, be predicted from readily mapped above-ground features, and is not only dictated by below-ground characteristics for which spatial data are lacking. Furthermore, our mechanistic estimate of winter survivorship was, on average, the third strongest predictor of winter occurrence probability across focal species. Winter distributions of North American bat species were driven by their physiological capacity to survive winter conditions and duration in a given location, as well as selection for topographic and other landscape features, but in species-specific ways. The influence of winter survivorship on several species' distributions, the underlying influence of climate conditions on winter survivorship, and the anticipated influence of WNS on bats' hibernation physiology and survivorship together suggest that North American bat distributions may undergo future shifts as these species are exposed to not only WNS, but climate change. We anticipate that the models presented here may offer a valuable baseline for assessing the potential species-level impacts of these stressors.</p>
Fig. 1 in New record of Rhinolophus chiewkweeae (Chiroptera: Rhinolophidae) from the east coast of Peninsular Malaysia with new information on their echolocation calls, genetics and their taxonomy
Fig. 1. Latero-ventral view of Rhinolophus chiewkweeae, from Sungai Buweh, Lawit
Think-aloud tests with information specialists of ai-systems Iris.ai and Yewno (Danish)
<p>This dataset contains the nine think-aloud tests conducted in April-June 2021. </p> <p>Think-aloud tests were designed to test the extent an academic search could be conducted in the two AI-powered search systems, Iris.ai (https://Iris.ai/) and Yewno Discover (https://www.yewno.com/discover). Pilot tests and validation tests were undertaken with two independent reviewers in March 2021. The validity tests were based on the principles outlined in Kim 2009, testing the content, face and construct validity of the test instrument (appendix 1). Consequently, the grammar and consistency of the language were improved and questions were reframed before the final think aloud tests were conducted in April-June 2021.</p> <p> </p> <p>Ten information specialists were invited to take part in the tests. One test person from the Yewno tests dropped out of the study, resulting in an overall drop-out rate of 10%. Accordingly, five think-aloud tests in Iris.ai and four in Yewno were held at the university libraries in Aarhus and Copenhagen.</p> <p>Two testers ran each test. One conducted the dialogue and guided the test person through the tasks set in the think-aloud test. The second, noted down the test persons behavior, humour and comments. All tests were recorded using Zoom, both audio, and screen were recorded as well as the test persons behaviour was observed. The recordings were saved to Edumedia for the duration of the project and destroyed thereafter. </p>
Supporting Information for Gas-phase formation of interstellar nucleobases from dehydrogenated radicals of formamide and vinyl cyanide
<p>Supporting Information for Gas-phase formation of interstellar nucleobases from dehydrogenated radicals of formamide and vinyl cyanide, including:</p> <p>1. Comparison between results obtained using M06 and MP2 methods.</p> <p>2. Energy diagrams of the reaction for transforming the reactants via H migration.</p> <p>3. Atomic coordinates data.</p>
Supplementary Information for "Semi-automated high-throughput substrate screening assay for nucleoside kinases
<p>This is the external Supplementary Information for our publication "Semi-automated high-throughput substrate screening assay for nucleoside kinases". Files are to follow soon. We apologize for the delay. Thank you for your patience.</p> <p> </p> <p>The preprint and the Supporting Information are available at ChemRxiv (https://doi.org/10.33774/chemrxiv-2021-k0w7q).</p>
Measuring phylogenetic information of incomplete sequence data
<p>Widely used approaches for extracting phylogenetic information from aligned sets of molecular sequences rely upon probabilistic models of nucleotide substitution or amino-acid replacement. The phylogenetic information that can be extracted depends on the number of columns in the sequence alignment and will be decreased when the alignment contains gaps due to insertion or deletion events. Motivated by the measurement of information loss, we suggest assessment of the Effective Sequence Length (ESL) of an aligned data set. The ESL can differ from the actual number of columns in a sequence alignment because of the presence of alignment gaps. Furthermore, the estimation of phylogenetic information is affected by model misspecification. Inevitably, the actual process of molecular evolution differs from the probabilistic models employed to describe this process. This disparity means the amount of phylogenetic information in an actual sequence alignment will differ from the amount in a simulated data set, which motivated us to develop a new test for model adequacy. Via theory and empirical data analysis, we show how to disentangle the effects of gaps and model misspecification. By comparing the Fisher information of actual and simulated sequences, we identify which alignment sites and tree branches are most affected by gaps and model misspecification.</p>
Figure 2 in Variation in elasmoid fish scale patterns is informative with regard to taxon and swimming mode
Figure 2. Landmark definitions.
Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes
<p>Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management, and infodemic controlling. However, predicting the scale of an information cascade in a long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the follower-followee network, retweet network, tweet content, and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension to information diffusion process and may assist in the control of such process.</p>
Supporting information for "Thermal alteration history of Fenghuoshan Group, Hoh Xil Basin, northern Tibetan Plateau: Insights from clumped isotope thermometry"
<p>The supporting information includes raw isotopic analysis data for "Thermal alteration history of Fenghuoshan Group, Hoh Xil Basin, northern Tibetan Plateau: Insights from clumped isotope thermometry" by Kong et al.</p>
Supporting Information for "Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons"
<p>This is the supporting information for the article "Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons". It contains:</p> <p>In Supporting_Information.pdf:</p> <p>1. A map of the correlation between descriptors.</p> <p>2. A spectral distribution.</p> <p>3. An explanation of the file example_code.zip.</p> <p>In example_code.zip:</p> <p>An example code of a machine learning model based on ECFP and corresponding input files.</p>
Plant species as the bear cuscus diet based on information from local guides
<p>The table contains information about the plant species as the bear cuscus diet in Bantimurung Bulusaraung National Park (BBNP) and Hasanuddin University Educational Forest (HUEF), South Sulawesi based on information from the local guides, as well as the parts of plants eaten.</p>
Fig. 2 in First Record of Riccardoella (Proriccardoella) triodopsis (Acariformes: Trombidiformes: Ereynetidae) from Japan, with Additional Morphological Information
Fig. 2. Gnathosoma of Riccardoella (Proriccardoella) triodopsis (female, 21441B). Scale bar: 10 µm.
Synthetic ground motions to support the Fennoscandian GMPEs. Supporting information – Response spectra dataset in excel format
<p>This dataset has been created in the NKS project: ”Synthetic ground motions to support the Fennoscandian GMPEs”, contract: NKS-R(18)126/5. The data is RotD50 (Boore, 2010, doi: <a href="https://doi.org/10.1785/0120090400">10.1785/0120090400</a>), pseudo-acceleration response spectra calculated from synthetic ground motions generated using physics-based modeling of Fennoscandian earthquakes. The earthquake magnitude range is 4.3-5.6; the rupture distance range 2-30km and the hypocenter depth range 2-20km. The response spectra is in mm/s2 and should be used up to 25Hz.</p> <p>Cite the data as part of the research report: Fülöp, L., Jussila, V., Fälth, B., Voss, P., Lund, B. 2019. Synthetic ground motions to support the Fennoscandian GMPEs. NKS - Nordic Nuclear Safety Research NKS-424, ISBN: ISBN 978-87-7893-514-4</p> <p>Methods used to generate the data are described in:</p> <p>Fülöp, L., Jussila, V., Lund, B., Fälth, B., Voss, P., Puttonen, J., Saari, J., and Heikkinen, P. 2017. Modelling as a Tool to Augment Ground Motion Data in Regions of Diffuse Seismicity – Final report. NKS - Nordic Nuclear Safety Research NKS-394, ISBN: 978-87-7893-482-6</p> <p>Fülöp, L., Jussila, V., Lund, B., Fälth, B., Voss, P., Puttonen, J., Saari, J., 2016. Modelling as a tool to augment ground motion data in regions of diffuse seismicity - Progress 2015. NKS Nordic Nuclear Safety Research, ISBN: 978-87-7893-448-2</p>
Dataset of Daphnia magna samples' information in mRNA ratio study
<p>Dataset of <em>Daphnia magna</em> samples' information in mRNA ratio study. Information including treatments, somatic growth rate, size, weight and mRNA ratios. </p>
MLR - Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams
<p> Supplementary artifact for Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams</p>
Supporting Information for the Journal Article "Automated Construction of Quantum–Classical Hybrid Models"
<p>This dataset contains the supporting information published together with the article "Automated Construction of Quantum–Classical Hybrid Models" (<a href="https://doi.org/10.1021/acs.jctc.1c00178"><em>J. Chem. Theory Comput.</em>, <strong>2022</strong>, <em>17</em>, 3797</a>).</p>
The SV information of A02 and the genotype information based on super-pangenome
<p>A02.SV.vcf is the SV information between the A02 genome and Nipponbare genome.</p> <p>238rice.all.bamSV.vcf is the raw data of the SV genotyping information based on the super pan-genome.</p> <p> </p>
Supporting Information - Fabbri et al. 2022 - Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment
<p>The supporting information of the journal article "Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment" from Fabbri et al. (2022) includes one file with the following content:</p> <p>S1 Details of consequential modelling: feedstock<br> S1.1 Identification type of changes (demand or supply)<br> S1.2 Identification of constrains in the market<br> S1.3 Identification of product substitutions<br> S1.4 Identification of affected production technology<br> S1.5 Identification of marginal crop and marginal supplier<br> S2 Details of consequential modelling: by-products<br> S3 Model parameters and unit processes<br> S3.1 Sugar beet<br> S3.2 Sugar cane<br> S3.3 Wheat<br> S3.4 Maize<br> S3.5 Wood<br> S3.6 Residual woodchips and sawdust<br> S4 Review of land use change accounting methods<br> S4.1 Direct land use change (dLUC)<br> S4.2. Indirect land use change (iLUC)<br> S5 Additional results<br> S5.1 Influence of spatial differentiation in LCIA<br> S5.2 Influence of indirect land use change (iLUC)<br> S6 References</p>
Financial Literacy Bibliometrics - Main Information
<p>Financial Literacy Bibliometrics - Main Information, part of PhD Thesis in Management.</p>
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.