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Review of Recent Trends in Measuring the Computing Systems Intelligence-igure 2. Intelligence of different living creature (accessed 01.11.2017). 2.1. A crow solving a complex task (https://www.disclose.tv/spooky-genius-crow-had-to-be-removed-from-scientific-experiment- 314886). 2.2. A group of dolphins with a social behaviour (http://www.sciencemag.org/news/2012/04/teamwork-builds-big-brains); 2.3. An orangutan that use a spear to fish (https://primatology.net/2008/04/29/orangutan-photographed-using-tool-as-spear-to-fish)
<p>Some species of birds have been shown capable of using different tools. Many studies consider the crows as very intelligent. Smirnova, Lazareva, and Zorina (2000) suggested that crows have some kind of numerical ability. Figure 2.1 presents a crow that uses a tool, a small stone in order to catch a worm from a glass of water.The dolphins in many studies are considered intelligent at the individual level. An advanced ability of dolphins is the self-awareness. Marten and Psarakos (1995) presented an interesting study based on self-view television to distinguish between self-examination and social behavior in the Bottlenose dolphin. The most well-known abilities of dolphins are to teach, learn and cooperate. Dolphins have a complex communication and social behaviour. Figure 2.2 presents the image of a common group of dolphins. Some studies prove that primates are one of the most intelligent in the class of animals (Reader, Hager, & Laland, 2011). Orangutans are one of the most intelligent primates. The ability of orangutans to use different types of tools in order to perform tasks is well-known. Figure 2.3 presents an orangutan that uses a spear to catch fish. The orangutans can be considered intelligent at individual level.</p>
ACE gene haplotypes and social networks: Using a biocultural framework to investigate blood pressure variation in African Americans
<p>This dataset contains all processed data used in analyses reported in the manuscript, ACE gene haplotypes and social networks: Using a biocultural framework to investigate blood pressure variation in African Americans. This project is part of a larger study focused on investigating the role of stress, discrimination, genetic variants, and other sociocultural factors in hypertension in African Americans.</p> <p> </p> <p> </p> <p>Variable Names and Descriptions:</p> <p>HHID: Participant Identification number</p> <p>sbp-10: Average of two Systolic Blood Pressure readings without 10 point correction for blood pressure medication (mmHg)</p> <p>sbp: Average of two Systolic Blood Pressure readings with 10 point correction for blood pressure medication (mmHg)</p> <p>dbp-5: Average of two Diastolic Blood Pressure readings without 5 point correction for blood pressure medication (mmHg)</p> <p>dbp: Average of two Diastolic Blood Pressure readings with 5 point correction for blood pressure medication (mmHg)</p> <p>ACE.Genotype: ACE genotype (0= Deletion/Deletion, 1= Insertion/Deletion, 2= Insertion/Insertion)</p> <p>WNK1.Genotype: WNK1 Genotype (0= Deletion/Deletion, 1= Insertion/Deletion, 2= Insertion/Insertion)</p> <p>age: age (years)</p> <p>sex: sex (Male =1, Female = 2)</p> <p>bpmedtake: Whether the participant uses blood pressure medication</p> <p>bmi: Body Mass Index (calculated from height and weight)</p> <p>ALTER GENDER|Answer:Male|Value:0|Count: Number of male alters (social network members)</p> <p>Alter Gender Male: Percentage of alters that are male</p> <p>ALTER GENDER|Answer:Female|Value:1|Count: Number of female alters (social network members)</p> <p>Alter Gender Female: Percentage of alters that are female</p> <p>Closeness_Mean: Average closeness centrality of the network</p> <p>Between_Mean: Average between-ness centrality of the network</p> <p>percentage of family in structural percentage: Percentage of central network positions occupied by family members</p> <p>relationship max close family: The most close (centrally) network member is a family member</p> <p>Average distance: Average distance of individuals in a network</p> <p>Hap B: ACE gene haplotypes</p> <p> </p>
Supplemental Materials to Accompany: Abowd and Schmutte "An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices"
<p>Materials to supplement "<a href="https://www.aeaweb.org/articles?id=10.1257/aer.20170627">An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices</a>" by John Abowd and Ian Schmutte.</p>
Dataset for paper: The spread of low-credibility content by social bots
<p>Data for the paper <em>The spread of low-credibility content by social bots </em>by Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer</p> <p>Code is available at https://github.com/IUNetSci/HoaxyBots</p>
Annexes du projet de recherche " Facteurs déterminants du processus décisionnel de la prise en charge du hallux valgus par les ostéopathes; une approche par la théorie du jugement social"
<p>Les fichiers disponibles sont les annexes du projet de recherche "Facteurs déterminants du processus décisionnel de la prise en charge du hallux valgus par les ostéopathes; une approche par la théorie du jugement social" écrit par Camie Käppeli, étudiante en Master en sciences mention ostéopathie, et supervisé par Paul Vaucher.</p> <ul> <li>Protocole</li> <li>Site web externe avec explications de l'étude (1ère étape)</li> <li>Questionnaire 1 + données personnelles (1ère étape)</li> <li>Rapport du questionnaire 1 (1ère étape)</li> <li>Questionnaire 2 (1ère étape)</li> <li>Rapport du questionnaire 2 (1ère étape)</li> <li>Site externe avec explications de l'étude (2ème étape)</li> <li>Exemple questionnaire (2ème étape)</li> <li>Rapport du questionnaire sur la non-participation au test pilote des vignettes cliniques</li> <li>Analyses STATA sous forme txt.</li> </ul>
International Journal of Applied Research in Social Sciences
<p><strong>International Journal of Applied Research in Social Sciences</strong></p> <p><strong>(</strong><a href="http://www.fepbl.com/ijarss"><strong>www.fepbl.com/ijarss</strong></a><strong>)</strong></p> <p><strong>Fair East Publishers</strong></p> <p>Call for Papers</p> <p>Dear Author/Researcher,</p> <p>IJARSS is a double blind peer reviewed International journal and is an initiative of Fair East Publishing. The journal aims to provide a platform for sharing latest knowledge of social sciences. The scope of the journal include but not limited to sociology, psychology, management, history, archaeology, anthropology, media studies, communication, languages, and so on. Kindly prepare your paper according to the ‘Authors Guidelines’ and send it via email. There is no submission fee or publication fee for inaugural session.</p> <p>With Warm Regards</p> <p>M. Tahir</p> <p>Editor-In-Chef</p> <p>International Journal of Applied Research in Social Sciences</p> <p>Fair East Publishing</p> <p><a href="http://www.fepbl.com">www.fepbl.com</a></p> <p>Manuscript Submission to: <a href="mailto:editor.ijarss@fepbl.com">editor.ijarss@fepbl.com</a></p>
Social media statistics
<p>This data was collected/ generated through the periodic monitoring of the project’s social media statistics (including Facebook, Twitter and LinkedIn) with a view to measuring and assessing the performance and results of the project’s social media activity in terms of dissemination and communication.</p>
Emotion recognition: social appraisal and offset task, 8-12 yo.
<p>Data from social appraisal task and offset task in 57 8-12 yo. school children, with pedagogy school type information.</p>
How Confidence in Prior Attitudes, Social Tag Popularity, and Source Credibility Shape Confirmation Bias Toward Antidepressants and Psychotherapy in a Representative German Sample: Randomized Controlled Web-Based Study
<p>ABSTRACT</p> <p>Background: In health-related, Web-based information search, people should select information in line with expert (vs nonexpert) information, independent of their prior attitudes and consequent confirmation bias.</p> <p>Objective: This study aimed to investigate confirmation bias in mental health–related information search, particularly (1) if high confidence worsens confirmation bias, (2) if social tags eliminate the influence of prior attitudes, and (3) if people successfully distinguish high and low source credibility.</p> <p>Methods: In total, 520 participants of a representative sample of the German Web-based population were recruited via a panel company. Among them, 48.1% (250/520) participants completed the fully automated study. Participants provided <em>prior attitudes</em> about antidepressants and psychotherapy. We manipulated (1) <em>confidence</em> in prior attitudes when participants searched for blog posts about the treatment of depression, (2) <em>tag popularity</em> —either psychotherapy or antidepressant tags were more popular, and (3) <em>source credibility</em> with banners indicating high or low expertise of the tagging community. We measured <em>tag</em> and <em>blog post</em> selection, and <em>treatment</em><em>efficacy ratings</em> after navigation.</p> <p>Results: Tag popularity predicted the proportion of selected antidepressant tags (beta=.44, SE 0.11; <em>P</em><.001) and blog posts (beta=.46, SE 0.11; <em>P</em><.001). When confidence was low (−1 SD), participants selected more blog posts consistent with prior attitudes (beta=−.26, SE 0.05; <em>P</em><.001). Moreover, when confidence was low (−1 SD) and source credibility was high (+1 SD), the efficacy ratings of attitude-consistent treatments increased (beta=.34, SE 0.13; <em>P</em>=.01).</p> <p>Conclusions: We found correlational support for defense motivation account underlying confirmation bias in the mental health–related search context. That is, participants tended to select information that supported their prior attitudes, which is not in line with the current scientific evidence. Implications for presenting persuasive Web-based information are also discussed.</p> <p>Trial Registration: ClinicalTrials.gov NCT03899168; https://clinicaltrials.gov/ct2/show/NCT03899168 (Archived by WebCite at http://www.webcitation.org/77Nyot3Do)</p> <p>J Med Internet Res 2019;21(4):e11081</p> <p>doi:10.2196/11081</p>
Social websites reviews and ratings of Dublin restaurants situated across 65 locations
<p>The dataset contains user reviews and ratings of restaurants situated across 65 locations in Dublin along with other features such as cuisine, price range etc. which are as follows.</p> <p> </p> <p><strong>Restaurant ID</strong>: Each ID represents a restaurant which has been given a unique ID to easily identify them. There are 211 restaurants in total.</p> <p> </p> <p><strong>Location ID</strong>: The locality in which each restaurant is situated have been given ID’s. 65 such unique locations are present in the data. </p> <p> </p> <p><strong>Review</strong>: This field represents the user reviews for each restaurant. Reviews have been cleaned to remove punctuations, emojis and special characters.</p> <p><br> <strong>Cuisine</strong>: Represents the cuisine type for each restaurant.</p> <p><strong>Price</strong> <strong>Range</strong>: The average price for dining in the restaurant. It has 3 categories: </p> <p>€30 and under | €31 to €50 | €51 and over</p> <p> </p> <p><strong>Food</strong> <strong>Rating</strong>: User given rating between 1 and 5 for the quality of food in the restaurant.</p> <p> </p> <p><strong>Service Rating</strong>: User given rating between 1 and 5 for the quality of service in the restaurant.</p> <p> </p> <p><strong>Ambience Rating</strong>: User given rating between 1 and 5 for the ambience in the restaurant.</p> <p> </p> <p><strong>Overall Rating</strong>: User given rating between 1 and 5 for the restaurant in total.</p> <p> </p> <p><strong>Restaurant Rating</strong>: The average rating of each restaurant based on general opinion.</p> <p> </p> <p><strong>Review Sentiment</strong>: Overall sentiment of the user review i.e. either Positive or Negative.</p> <p> </p> <p><strong>Total Number of Rows</strong>: 10000</p> <p> </p> <p><strong>Number of Rows for Positive Sentiment</strong>: 5000</p> <p> </p> <p><strong>Number of Rows for Negative Sentiment</strong>: 5000</p> <p> </p> <p><strong>Number of Unique Restaurants</strong>: 211</p> <p> </p> <p><strong>Number of Unique Locations</strong>: 65</p> <p> </p> <p><strong>Number of Unique Cuisines</strong>: 43</p>
Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation
<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts’ evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts’ blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants’ treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em><.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em><.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em><.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s>.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated—and even eliminated—when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>
Interspecific social interactions shape public goods production in natural microbial communities
<p>The R code (Hesse_etal.R) descibes step by step how metal polution affects the ecology and evolution of a community-wide public good – the production of metal-detoxifying siderophores. This code accompanies the manuscript "Interspecific interactions shape public goods production in natural microbial communities" (https://www.biorxiv.org/content/10.1101/710715v1).</p> <p>The R code is divided into different sections per figure (1-5 and supplementary Figure S1). Each section first starts with reading in the appropriate data files (cvs files) and continues with statistics and plotting of figures.</p>
Network data for the paper: Intellectual and social similarity among scholarly journals.
<p>Network data used for the analysis contained in Baccini A, Barabesi L, Gingras Y, Kalfaoui M (2019) Intellectual and social similarity among scholarly journals. An exploratory comparison of the networks of editors, authors and co-citations.</p> <p>Data are in .net format for Pajek software</p> <p>CC indicates co-citation network.</p> <p>IA indicated Interlocking authorship network.</p> <p>IE indicates interlocking editorship network.</p> <p>Stat is for statistics; Econ is for economics; ILS is for information and library science.</p> <p> </p> <p> </p>
PERCEIVE The use of social media in EU policy communication and implications for the emergence of a European public sphere
<p>This data set contains the underlying data of the paper “<strong>The use of social media in EU policy communication and implications for the emergence of a European public sphere</strong>”, published by The Journal of Regional Research - Investigaciones Regionales (ISSN: 1695-7253; E-ISSN: 2340-2717) in 2019.</p> <p>Data openly available within this dataset are a subset of the two following data sets, which contains all the relevant data of Work Package 3 and Work Package 5 of PERCEIVE project:</p> <ul> <li>Data set:<strong> “PERCEIVE: WP3: Effectiveness of communication strategies of EU projects” </strong><a href="https://doi.org/10.5281/zenodo.3371133">https://doi.org/10.5281/zenodo.3371133</a></li> <li>Data set:<strong> “PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels” </strong><a href="https://doi.org/10.5281/zenodo.3371174">https://doi.org/10.5281/zenodo.3371174</a></li> </ul> <p>For the paper we collected Facebook posts referred to EU CP policies. We don’t have the permission to share these data (as they are protected by copyright), but all the sources are described in Deliverable 5.2, which is public (see <a href="http://doi.org/10.6092/unibo/amsacta/5726">http://doi.org/10.6092/unibo/amsacta/5726</a> or <a href="http://doi.org/10.5281/zenodo.1318184">http://doi.org/10.5281/zenodo.1318184</a>). We analyzed the textual content of data to construct a database of discursive topics in Task5.4. Data set includes the results of topic modeling and of a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions. </p>
Detecting weak and strong Islamophobic hate speech on social media
<p>Data, code and annotation guidelines for our publication, 'Detecting weak and strong Islamophobic hate speech on social media' (2019).</p>
Raw Results of the paper "Automated Synthesis of Social Laws in STRIPS"
<p>The following file contains the empirical evaluation of the algorithm that was presented in the paper "Automated Synthesis of Social Laws in STRIPS" (AAAI-20) on several PDDL benchmarks. </p>
The ecosystem of technologies for social science research, data
<p>This is the list of 417 software tools, packages, apps and platforms we have reviewed as part of SAGE Ocean. The dataset contains a number of features: name, pitch, type of tool, country, year, some papers, funders, founders, founding team etc. The latest version of this list will be available on github along with the metadata: <a href="https://github.com/danielagduca/SAGE_tools_social_science/tree/master/data">https://github.com/danielagduca/SAGE_tools_social_science/tree/master/data</a></p> <p>The supporting white paper describing the tools is:</p> <p>Duca, D., & Metzler, K. (2019). *The ecosystem of technologies for social science research* (White paper). London, UK:<br> Sage. doi: 10.4135/wp191101</p>
Figure 1 in The first record of the deep-sea jellyfish Stygiomedusa gigantea (Scyphozoa: Semaeostomeae) from the tropical Southwestern Atlantic found on social media
Figure 1. Image of Syigiomedusa gigantea individuals observed in (A) Brazil, 12°34'39"S, 38°00'19"W, at the water surface, present study; (B) Gulf of Mexico, 26°12.483'N, 91°26.583'W, at 1747 m, from Benfield and Graham (2010); (C) Gulf of California, 25°27.220'N, 109°50.170', at 1300 m, from Drazen and Robinson (2004). The scale (= 1 m) is only applicable for Fig. 1A.
Fig. 46. Partial truncated map for Central and South America with species distributions for the M in Taxonomic notes on social wasps of the groups of Mischocyttarus wagneri (Buysson 1908) and M. barbatus Richards 1945 (Hymenoptera, Vespidae, Polistinae)
Fig. 46. Partial truncated map for Central and South America with species distributions for the M. barbatus group, and with the pooled distribution of the group of M. wagneri (see next figure for detailed representation of distributions of species of this group).
Fig. 47 in Taxonomic notes on social wasps of the groups of Mischocyttarus wagneri (Buysson 1908) and M. barbatus Richards 1945 (Hymenoptera, Vespidae, Polistinae)
Fig. 47. Map detail of southeastern South America (with partial view of some Brazilian states) with species distributions for the M. wagneri group. MG: Minas Gerais, RJ: Rio de Janeiro, SP: São Paulo, PR: Paraná, SC: Santa Catarina, RS: Rio Grande do Sul.
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.