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FIGURE 2 in On the need to follow rigorously the Rules of the Code for the subsequent designation of a nucleospecies (type species) for a nominal genus which lacked one: the case of the nominal genus Trimeresurus Lacépède, 1804 (Reptilia: Squamata: Viperidae)
FIGURE 2. Trimeresurus viridis. Lacépède, 1804. Lectophoront, MNHN 4057. Lateral view of head, left side. Photograph by Patrick David.
FIGURE 1 in On the need to follow rigorously the Rules of the Code for the subsequent designation of a nucleospecies (type species) for a nominal genus which lacked one: the case of the nominal genus Trimeresurus Lacépède, 1804 (Reptilia: Squamata: Viperidae)
FIGURE 1. Trimeresurus viridis. Lacépède, 1804. Lectophoront, MNHN 4057. General view. Photograph by Patrick David.
FIGURE 4 in On the need to follow rigorously the Rules of the Code for the subsequent designation of a nucleospecies (type species) for a nominal genus which lacked one: the case of the nominal genus Trimeresurus Lacépède, 1804 (Reptilia: Squamata: Viperidae)
FIGURE 4. Trimeresurus viridis. Lacépède, 1804 Lectophoront, MNHN 4057. Venter. Photograph by Patrick David.
Dataset about how children with neuromotor disabilities deal with technological games and their needs and requirements for inclusive videogames
<p>This dataset includes answers to a survey developed in Redcap to investigate how children with neuromotor impairment deal with technology-based play and what they need to be included in an engaging ludic activity with their peers, thus defining the requirements of new accessible and inclusive videogames.</p> <p>The survey was co-designed by end users together with expert clinicians, engineers, and designers. The survey included open and closed-ended questions and 5-point Likert scales.</p> <p>The survey was distributed in April 2020.</p> <p>56 families with a child with disability answered the online survey in anonymized form. </p> <p>This dataset can be useful for videogame developers and console designers, since it gives useful suggestions for developing technologies which are enjoyable, accessible and inclusive at the same time</p>
An interactive tool to forecast us hospital needs in the Coronavirus 2019 pandemic
<p>We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing COVID-19 pandemic. Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from seven COVID-19 models, customize a large set of parameters, examine trends in testing and hospitalization, and download forecast data.</p> <p>The data and scripts contained herein are used to generate Figure 1 of the associated manuscript, which presents general forms of the models used by our application and presents results for each model across time.</p>
Climate, caribou and human needs linked by analysis of Indigenous and scientific knowledge
<p><span>Migratory tundra caribou are ecologically and culturally critical in the circumpolar North. However, they are declining almost everywhere in North America, likely due to natural variation exacerbated by climate change and human activities. Yet, the interconnectedness between climate, caribou, and human well-being has received little attention. To address this gap, we bridged</span><span> </span><span>Indigenous and scientific knowledge in a single model, using as an example the Porcupine caribou herd social-ecological system. Our analysis, involving </span><span>688 (fall season) and 616 (spring season) interviews conducted over nine years with 405 (fall season) and 390 (spring season) Indigenous hunters </span><span>from nine communities, demonstrates that </span><span>environmental conditions, </span><span>large-scale temporal changes associated with caribou demography, and cultural practices </span><span>affect hunters' capacity to meet their needs in caribou. </span><span>Our quantitative approach </span><span>bolsters our understanding of the complex relationships between ecosystems and human welfare in environments exposed to rapid climate change and shows </span><span>the benefits of long-term participatory research methods implemented by Indigenous and scientific partners.</span></p>
Keep talking, I need to check my phone! Online Vigilance and Phubbing: The Mediating Role of Loneliness and the Moderating Role of Moral Disengagement
<p>In the present study, we investigated the relationship between online vigilance and phubbing, a specific form of technoference that implies ignoring someone while favoring technological, Internet-based devices, such as smartphones.</p>
Supporting datasets for "The need for Operando Modelling of 27Al NMR in Zeolites"
<p>tar of directory tree containing input files for VASP and CASTEP calculations of local geometries and NMR parameters (both static and dynamic) for systems described in the manuscript, including MOR and CHA in H and Na form and various water loadings.</p>
Explanation Needs in App Reviews: Taxonomy and Automated Detection
<p><strong>Replication package for our paper submission to RE 2023</strong></p> <p>It contains the following files:</p> <ol> <li>Our dataset of 5,564 app reviews that we manually labeled with respect to the tags "explanation need present" and "explanation need not present".</li> <li>Code/notebooks that we used for the training and evaluation of our explanation need detection approaches</li> <li>.bin file of our best performing model</li> </ol>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Formalised Information Needs dataset
<p>Dataset for the following publication:</p> <pre>@inproceedings{JCDL23_KreutzBSSW, author = {Christin Katharina Kreutz and Martin Blum and Philipp Schaer and Ralf Schenkel and Benjamin Weyers}, title = {Evaluating Digital Library Search Systems by using Formal Process Modelling}, booktitle = {{JCDL} '23} }</pre> <p>The dataset contains evaluation data from an user study with 13 participants working on two tasks: expert search (T_ex) and paper search (T_pa). For participants there are three BPMN models per task, depicting <em>1)</em> their ideal task conduction model (vIMM), <em>2)</em> the expert-generated translation of this model to the SchenQL digital library system (vPGM) and <em>3)</em> the participants' task conduction model using SchenQL (PCM). These BPMNs are given as SVGs in folders corresponding to the tasks(<em>T_ex</em> and <em>T_pa</em>) inside folders corresponding to participants' code names.</p> <p>For all participants the transcript of the first user session is contained as text files in a folder named <em>Transcripts Session 1</em>. Additionally, participants' responses to the questionnaires are included (in <em>Questionnaires.csv</em>).</p> <p> </p> <p>Extended version: <a href="../records/10791641">Re-FIND</a></p>
Figure 2 in Endemic lineages of spiny frogs demonstrate the biogeographic importance and conservational needs of the Hindu Kush-Himalaya region
Figure 2. Minimum-spanning haplotype networks of Allopaa hazarensis generated for 16S and COI sequence data with the number of used sequences, detected haplotypes, and the level of nucleotide variability. Symbol sizes reflect haplotype frequencies, and a small black line between two haplotypes corresponds to one mutation step. Sequence-IDs are indicated for each haplotype (h1–h8). Map shows the localities from where the respective haplotypes originate.
Figure 3 in Endemic lineages of spiny frogs demonstrate the biogeographic importance and conservational needs of the Hindu Kush-Himalaya region
Figure 3. Distribution map for Allopaa hazarensis (A) and Chrysopaa sternosignata (B) derived from species distribution model (SDM) using MAXENT, including known records of the species (red = A. hazarensis, green = C. sternosignata). Photo credit: D. Jablonski.
Figure 1 in Endemic lineages of spiny frogs demonstrate the biogeographic importance and conservational needs of the Hindu Kush-Himalaya region
Figure 1. Bayesian inference (BI; left) and maximum likelihood tree (ML; right) based on concatenated mtDNA and nDNA sequence data (16S + COI + Rag1) of the tribe Paini. Numbers at branch nodes refer to posterior probabilities ≥ 0.9 (BI tree), as well as Felsenstein's bootstrap values ≥ 70% and transfer bootstrap expectation ≥ 0.9 (ML tree). Branches of Allopaa hazarensis are indicated red, while Chrysopaa sternosignata is highlighted green. Species names are followed by voucher number (if available). Coloured shaded boxes indicate subgroups of Nanorana and the new clade (in yellow) with so far unidentified specimens.
.RData objects needed to reproduce ST vignette
<p>BC.Rdata: Seurat Object</p> <p>ST_expr_smooth_out.RData: Pre-computed smooths spatial transcriptomics gene expression using the weighted mean of neighbouring spots in one compartment.</p>
Tuesday 5 May: You shouldn't need to be a web historian to use web archives, Ian Milligan U Waterloo
<p>Keynote: You shouldn’t need to be a web historian to use web archives: Lowering barriers to access through community and infrastructure, Ian Milligan, Associate Professor, Department of History at University of Waterloo</p>
Maximizing Societal Benefit Across Multiple Hyperspectral Earth Observation Missions: A User Needs Approach - DATA and CODES
<p>In this repository you will find data elicited from Italian and the NASA Surface Biology and Geology (SBG) Designated Observable users. the first page "read me first" provides a description of the first sheet "User requirements merged" where you will find the reqirements codified for both community of users.</p> <p>Other files include the codes used throug the R software to develop several useful figures.</p>
CO2SMOS H2020 Project_Elicitation of Stakeholders and Market needs_webinars survey datasets
<p><span>CO2SMOS’ Task 1.1 aims at defining the stakeholders’ (technology providers, feedstock suppliers, refineries, fuel traders, final end-users, etc.) requirements and specifications, emphasizing on the expected benefits from the CO2SMOS CO2-to-Chemicals Platform concept.</span></p> <p><span>These datasets gathers the data collected by external stakeholders who participated in the first webinar organized on September 2021 and the second webinar organized on October 2021 regarding their industrial vision and market needs</span></p>
Symptom Burden and Unmet Supportive Care Needs in Lung Cancer Patients Undergoing First or Second Line Immunotherapy
ClinicalTrials.gov study NCT03741868. IPD Sharing: NO. Countries: 1. Publications: 2.
Multi-level Integration for Patients With Complex Needs Facilitated by ICTs. A Shared Approach, Mutual Learning and Evaluation Are Expected to Create Synergies Among the Partners and to Bring Forward
ClinicalTrials.gov study NCT03042039. IPD Sharing: NO. Countries: 5. Publications: 8.
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.