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FIGURE 4 in A protocol for online documentation of spider biodiversity inventories applied to a Mexican tropical wet forest (Araneae, Araneomorphae)
FIGURE 4. Species richness estimations. Red triangles with values indicated with (*) correspond to the species estimations for those treatments fitted to a lognormal distribution. White and red triangles over the x axis indicate higher overestimations.
FIGURE 5. Species richness estimations slopes. Jack 2s in A protocol for online documentation of spider biodiversity inventories applied to a Mexican tropical wet forest (Araneae, Araneomorphae)
FIGURE 5. Species richness estimations slopes. Jack 2s indicates the slope average for these estimations based on incidence (BAT) and abundance (Bat and Estimates). Same averages were calculated for Jack 1s and Chao 1 and 2 estimation slopes. Column indicated with (*) correspond to the slope value for pitfalls under Chao's estimations.
Online Appendix - Pricing in Integrated Heat and Power Markets
<p>Online Appendix for the letter "Pricing in Integrated Heat and Power Markets". This appendix contains the nomenclature, generation data, and table with the results of the test cases.</p>
Crowd-Annotation Results: Identifying and Classifying User Requirements in Online Feedback
<p>Results from the Figure Eight experiment conducted as part of the paper "Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing" published at REFSQ 2020.</p>
Supplementary online material for PhD thesis manuscript Proteomic approaches to the characterization of tolerance and virulence in bacterial biofilms
<p>These data belong to a PhD thesis manuscript Proteomic approaches to the characterization of tolerance and virulence in bacterial biofilms.</p> <p> </p> <p><strong>Supplementary online material 1</strong></p> <p>Theoretical proteome of <em>Staphylococcus aureus </em>ATCC 25923.</p> <p><strong>Supplementary online material 2</strong></p> <p>Theoretical proteome of <em>Pseudomonas aeruginosa </em>PAO1.</p> <p><strong>Supplementary online material 3</strong></p> <p>MaxQuant (v. 1.6.1.0) output of exoproteomic analysis carried out on a dual-species biofilm model.</p> <p><strong>Supplementary online material 4</strong></p> <p>MaxQuant (v. 1.6.1.0) output of surfaceomic analysis carried out on a dual-species biofilm model</p> <p><strong>Supplementary online material 5</strong></p> <p>Curated MaxQuant (v. 1.6.1.0) output of exoproteomic analysis carried out on a dual-species biofilm model.</p> <p><strong>Supplementary online material 6</strong></p> <p>Curated MaxQuant (v. 1.6.1.0) output of surfaceomic analysis carried out on a dual-species biofilm model.</p> <p><strong>Supplementary online material 7</strong></p> <p>Protein quantification of valid identifications in LC-MS/MS analysis of <em>Staphylococcus aureus </em>and <em>Pseudomonas aeruginosa </em>dual-species biofilms.</p>
Mapas ilustrativos del proceso de adaptación de urgencia a la evaluación online en entornos habitualmente presenciales
<p>Mapas conceptuales de la Guía de recomendaciones para la evaluación online en las Universidades Públicas de Castilla y León</p>
Data of "Implementation of stimuli with millisecond timing accuracy in online experiments"
<p>Data of "Implementation of stimuli with millisecond timing accuracy in online experiments"</p>
Data from: From trial to implementation, bringing team-based learning online – Duke-NUS Medical School's response to the COVID-19 pandemic
<p>The restrictions imposed by the COVID-19 pandemic resulted in Duke-NUS Medical School moving all their lessons online. Duke-NUS employs a team-based learning (TBL) pedagogy, which depends heavily on student discussion. In 2015, our university had implemented an eLearning week where lessons were conducted online. Using the already present online assessment processes, the data, insights and student feedback allowed for swift implementation of an online TBL module for home-based learning in response to the pandemic in 2020. These protocols were modified over the weeks, guided by feedback from students and faculty. An analysis of this online TBL module is presented herein.</p>
Online Resource 2 - Radial displacement at the tunnel wall of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)
<p>The radial displacement at the tunnel wall at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included.</p>
Online Resource 1 – Maximal hoop stress developed in the lining of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)
<p>The maximal hoop stress developed in the lining of a tunnel at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included. <strong>A version 2 of the document with some minor corrections has been published. </strong></p>
FUNCERT - Registration dynamics and course certification rates on a Massive Open Online Course (MOOC) platform
<p>The FUNCERT data set describes 1 million course registration events collected over a 2-year period from the MOOC platform FUN (France Université Numérique, fun-mooc.fr). Each registration event is associated with a timestamp and an indication of whether a completion certificate was ultimately awarded. It was used in the referenced manuscript to model the impact of multiple course registrations on certificate rates.</p> <p>The data set is provided as a single file (FUNCERT.csv) with the following fields.</p> <p><strong>Time (min)</strong> The time of the registration event reported in minutes elapsed since the beginning of data collection. The time stamp is reported as a relative time only, not an exact calendar date, to enhance anonymization.</p> <p><strong>User ID</strong> A unique ID number for each user of the platform during this period. The number has been generated specifically for this data set and does not correspond to user ID numbers used within the FUN platform.</p> <p><strong>Course ID</strong> Each of the 140 offered courses is assigned a unique and anonymous ID code. In some cases courses were offered multiple times within the data collection period. The same Course ID is used across multiple instances of the same course.</p> <p><strong>Certificate</strong> A boolean variable set to 1 if the registration event is associated with a course completion certificate. Of 140 offered courses, 91 awarded certificates during the study period.</p> <p><strong>Age</strong> User age in years at the time of registration. Field may be left blank if data was not provided.</p> <p><strong>Gender</strong> User gender as selected from the options m and f. Field may be left blank if data was not provided.</p>
FIGURE 7 in An inventory of online reptile images
FIGURE 7. The distributions of species and images. (A) The number of species with zero images, with three hotspots for missing species highlighted. (B) The percentage of species with photos, with three cold spots highlighted. A and B are limited to the 10,064 species included in the GARD dataset (Roll et al. 2017). Spatial resolution is 0.1667 degrees. (C) The number of species with photos, on a per country basis, with hotspot highlighted (Mexico, South Africa, Australia). Note the colour scheme is square-rooted to aid differentiation of lower values. (D) The percentage of a country's species with images, with cold spots highlighted (from left to right: Haiti, Somalia, and Papua New Guinea).
FIGURE 2 in An inventory of online reptile images
FIGURE 2. Number of species with images on each of the data sources, as well as the total number of species across all sources (right panel).
FIGURE 4 in An inventory of online reptile images
FIGURE 4. Overlap of species content in the 6 repositories used. For instance, 749 species (top right) have only photos in the Reptile Database while 635 species (bottom right) have photos both in the Reptile Database and iNaturalist, but not in any other repository. 295 species (middle right) have photos in the Reptile Database, iNaturalist, and Flickr, and so on. Numbers>99 are printed in larger font to emphasize their disproportional contribution.
FIGURE 6 in An inventory of online reptile images
FIGURE 6. Total number of species (grey), number of species with photos (red), and percentage of species with photos per family from any of the data sources. Note different X-axis scales for each of the three clades shown (Crocodylia, Lepidosauria, Testudines).
FIGURE 3 in An inventory of online reptile images
FIGURE 3. Number of images per species by source, excluding species without images. Top five species with images are highlighted with species labels.
Students' Perspective on Online teaching in higher institutions during COVID 19
<p><strong>Nigeria Students' Perspective on Online teaching in higher institutions during COVID 19Students' Perspective on Online teaching in higher institutions during COVID 19</strong></p>
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
<p><strong>Motivation</strong></p> <p>The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.</p> <p><strong>License</strong></p> <p>Creative Commons License.</p> <p><strong>Description of the dataset</strong></p> <p>In this work, we focused on collecting data from credit-based freemium services. We divide the datasets into two parts:</p> <p><strong>- Historical Data (</strong><strong>historical_anon.zip</strong><strong>)</strong></p> <p>This consists of all the data for Twitter and YouTube from blackmarket services gathered via sequential querying of the website’s URLs between the period March-June, 2019. We collected the metadata of each entity present in the historical data.</p> <p><strong>Twitter:</strong></p> <p>We collected the following fields for retweets and followers on Twitter:</p> <p><code>user_details</code>: A JSON object representing a Twitter user.</p> <p><code>tweet_details</code>: A JSON object representing a tweet.</p> <p><code>tweet_retweets</code>: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.</p> <ol> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object</a><a href="#fnref1">↩︎</a></p> </li> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object</a><a href="#fnref2">↩︎</a></p> </li> </ol> <p><strong>YouTube:</strong></p> <p>We collected the following fields for YouTube likes and comments:</p> <p><code>is_family_friendly:</code> Whether the video is marked as family friendly or not.</p> <p><code>genre:</code> Genre of the video.</p> <p><code>duration:</code> Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.</p> <p><code>description:</code> Description of the video.</p> <p><code>upload_date:</code> Date that the video was uploaded.</p> <p><code>is_paid:</code> Whether the video is paid or not.</p> <p><code>is_unlisted:</code> The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag <em>unlisted</em> indicates that the video can only be accessed by people who have a direct link to it.</p> <p><code>statistics:</code> A JSON object containing the number of dislikes, views and likes for the video.</p> <p><code>comments:</code> A list of comments for the video. Each element in the list is a JSON object of the text (<em>the comment text</em>) and time (<em>the time when the comment was posted</em>).</p> <p>We collected the following fields for YouTube channels:</p> <p><code>channel_description:</code> Description of the channel.</p> <p><code>hidden_subscriber_count:</code> Total number of hidden subscribers of the channel.</p> <p><code>published_at:</code> Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).</p> <p><code>video_count:</code> Total number of videos uploaded to the channel.</p> <p><code>subscriber_count:</code> Total number of subscribers of the channel.</p> <p><code>view_count:</code> The number of times the channel has been viewed.</p> <p><code>kind:</code> The API resource type (e.g., <em>youtube#channel</em> for YouTube channels).</p> <p><code>country:</code> The country the channel is associated with.</p> <p><code>comment_count:</code> Total number of comments the channel has received.</p> <p><code>etag:</code> The ETag of the channel which is an HTTP header used for web browser cache validation.</p> <p>The historical data is stored in five directories named according to the type of data inside it. Each directory contains JSON files corresponding to the data described above. <strong>'historical_sample.zip'</strong> contains a small sample of the historical dataset.</p> <p>- <strong>Time-series Data (time_series_anon.zip)</strong></p> <p>This consists of time-series data (collected every 8 hours) of Twitter users and tweets collected from the blackmarket services between the period of March-June, 2019. We collect the following time-series data for retweets and followers on Twitter:</p> <p><code>user_timeline</code>: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.</p> <p><code>user_followers</code>: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.</p> <p><code>user_followees</code>: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.</p> <p><code>tweet_details</code>: This is a JSON object representing a given tweet, collected after every time interval.</p> <p><code>tweet_retweets</code>: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.</p> <p>The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above. <strong>'time_series_sample.zip'</strong> contains a small sample of the time series dataset.</p> <p><strong>Data Anonymization</strong></p> <p>The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.</p> <p> </p>
Online Survey and Interview + Results
<p>The questions and results for both the online survey and the (semi-)structured interview.</p>
Kinematics and EMG to show integration of proprioceptive and visual feedback during online control of reaching
<p>Visual and proprioceptive feedback both contribute to optimal perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model (Experiment 1). In a control experiment we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign (Experiment 2). These results highlight a complex process for multi-sensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb. The dataset contains hand kinematics and EMG data recorded during each experiment, and information of visual/mechanical disturbances that were applied during the experiments.</p>
ScienceDex guides
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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.