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edi60/100

Social Network Survey of Forest Landowners in New Hampshire and Vermont 2010

Forests provide invaluable services, and nationally a significant portion of them are owned by millions of individual, private decision makers. Butler (2008) reports that the vast majority of owners (92%, representing 87% of all family forest lands) make management decisions for their land on their own, with a very small minority relying on the advice of professional foresters. Nationally, Butler (2008) goes on to report that 4 % of family forest owners (representing 17% of family forest land) have a professionally prepared management plan for their lands. It is clear that family forests are important, yet millions of owners are apparently not making decisions on the basis of professional advice. Our goal was to improve our understanding of who landowners seek information from when making decisions about their forestland. More specifically, our objective was to explore the possible role of egocentric social networks that landowners may rely upon for information when making a decision. Can we estimate their composition, the possible role of professionals, and the nature of the relationships, in terms of involvement, helpfulness, and trust? Finally, we explored landowner egocentric networks in two similar and adjacent states (Vermont and New Hampshire) that have different programmatic approaches to reaching landowners. Do these result in different ways that landowners acquire information? Using two different approaches through a mail survey methodology, we assessed the extent to which private woodland owners are connected to other people, the degree to which they are considered information sources, and the nature of their decision making behavior. Respondents consistently report valuable connections to non-professionals. They similarly report nominal contact to public foresters, whose role is to inspire prudent forest stewardship on privately held lands. This minimal contact is consistent in two different states with rather different programmatic goals (e.g., educatio

openCC0Dec 2023View details →

zenodo52/100

Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching

<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>

opencc-by-4.0Jul 2024View details →

zenodo48/100

Citation network data sets for 'Oxytocin – a social peptide? Deconstructing the evidence'

Introduction This note describes the data sets used for all analyses contained in the manuscript 'Oxytocin - a social peptide?’<a href="#_ftn1">[1]</a>  Data Collection The datasets described here were originally retrieved from Web of Science (WoS) Core Collection via the University of Edinburgh’s library subscription <a href="#_ftn2">[2]</a>. The aim of the original study for which these data were gathered was to survey peer-reviewed primary studies on oxytocin and social behaviour. To capture relevant papers, we used the following query: TI = (“oxytocin” OR “pitocin” OR “syntocinon”) AND TS = (“social*” OR “pro$social” OR “anti$social”) The final search was performed on the 13 September 2021. This returned a total of 2,747 records, of which 2,049 were classified by WoS as ‘articles’. Given our interest in primary studies only – articles reporting original data – we excluded all other document types. We further excluded all articles sub-classified as ‘book chapters’ or as ‘proceeding papers’ in order to limit our analysis to primary studies published in peer-reviewed academic journals. This reduced the set to 1,977 articles. All of these were published in the English language, and no further language refinements were unnecessary. All available metadata on these 1,977 articles was exported as plain text ‘flat’ format files in four batches, which we later merged together via Notepad++. Upon manually examination, we discovered examples of papers classified as ‘articles’ by WoS that were, in fact, reviews. To further filter our results, we searched all available PMIDs in PubMed (1,903 had associated PMIDs - ~96% of set). We then filtered results to identify all records classified as ‘review’, ‘systematic review’, or ‘meta-analysis’, identifying 75 records <a href="#_ftn3">[3]</a> (thus, ~4% of records classified by WoS were classified as reviews in PubMed). After examining a sample and agreeing with the PubMed classification, these were removed these from our dataset - leaving a total of 1,902 articles. From these data, we constructed two datasets via parsing out relevant reference data via the Sci2 Tool <a href="#_ftn4">[4]</a>. First, we constructed a ‘node-attribute-list’ by first linking unique reference strings (‘Cite Me As’ column in WoS data files) to unique identifiers, we then parsed into this dataset information on the identify of a paper, including the title of the article, all authors, journal publication, year of publication, total citations as recorded from WoS, and WoS accession number. Second, we constructed an ‘edge-list’ that records the citations from a citing paper in the ‘Source’ column and identifies the cited paper in the ‘Target’ column, using the unique identifies as described previously to link these data to the node-attribute-list. We then constructed a network in which papers are nodes, and citation links between nodes are directed edges between nodes. We used Gephi Version 0.9.2 <a href="#_ftn5">[5]</a> to manually clean these data by merging duplicate references that are caused by different reference formats or by referencing errors. To do this, we needed to retain both all retrieved records (1,902) as well as including all of their references to papers whether these were included in our original search or not. In total, this produced a network of 46,633 nodes (unique reference strings) and 112,520 edges (citation links). Thus, the average reference list size of these articles is ~59 references. The mean indegree (within network citations) is 2.4 (median is 1) for the entire network reflecting a great diversity in referencing choices among our 1,902 articles. After merging duplicates, we then restricted the network to include only articles fully retrieved (1,902), and retrained only those that were connected together by citations links in a large interconnected network (i.e. the largest component). In total, 1,892 (99.5%) of our initial set were connected together via citation links, meaning a total of ten papers were removed from the following analysis – and these were neither connected to the largest component, nor did they form connections with one another (i.e. these were ‘isolates’). This left us with a network of 1,892 nodes connected together by 26,019 edges. It is this network that is described by the ‘node-attribute-list’ and ‘edge-list’ provided here. This network has a mean in-degree of 13.76 (median in-degree of 4). By restricting our analysis in this way, we lose 44,741 unique references (96%) and 86,501 citations (77%) from the full network, but retain a set of articles tightly knitted together, all of which have been fully retrieved due to possessing certain terms related to oxytocin AND social behaviour in their title, abstract, or associated keywords. Before moving on, we calculated indegree for all nodes in this network – this counts the number of citations to a given paper from other papers within this network – and have included this in the node-attribute-list. We further clustered this network via modularity maximisation via the Leiden algorithm <a href="#_ftn6">[6]</a>. We set the algorithm to resolution 1, and allowed the algorithm to run over 100 iterations and 100 restarts. This gave Q=0.43 and identified seven clusters, which we describe in detail within the body of the paper. We have included cluster membership as an attribute in the node-attribute-list. For additional analysis, we also analysed the full reference list data to examine the most commonly cited references between 2016 and 2021 - the results of this are described in OTSOC_Cited_2016-2021.csv. This takes the reference lists of all retrieved papers within the network and examines their full reference lists (including references to other papers not contained within the network). These data were cleaned by matching DOIs and manual cleansing.  Data description We include here two network datasets: (i) ‘OTSOC-node-attribute-list.csv’ consists of the attributes of 1,892 primary articles retrieved from WoS that include terms indicating a focus on oxytocin and social behaviour; (ii) ‘OTSOC-edge-list.csv’ records the citations between these papers. Together, these can be imported into a range of different software for network analysis; however, we have formatted these for ease of upload into Gephi 0.9.2. Finally, we include (iii) 'OTSOC_Cited_2016-2021' that lists all papers cited by >10 papers in the OTSOC network following any analysis of the bibliographies of retrieved papers. Below, we detail their contents: 1. ‘OTSOC-node-attribute-list.csv’ is a comma-separate values file that contains all node attributes for the citation network (n=1,892) analysed in the paper. The columns refer to: Id, the unique identifier Label, the reference string of the paper to which the attributes in this row correspond. This is taken from the ‘Cite Me As’ column from the original WoS download. The reference string is in the following format: last name of first author, publication year, journal, volume, start page, and DOI (if available).  Wos_id, unique Web of Science (WoS) accession number. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag. Title, paper title. Authors, all named authors. Journal, journal of publication. Pub_year, year of publication. Wos_citations, total number of citations recorded by WoS Core Collection to a given paper as of 13 September 2021 Indegree, the number of within network citations to a given paper, calculated for the network shown in Figure 1 of the manuscript. Cluster, provides the cluster membership number as discussed within the manuscript (Figure 1). This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.43|7 clusters) 2. ‘OTSOC-edge -list.csv’ is a comma-separated values file that contains all citation links between the 1,892 articles (n=26,019). The columns refer to: Source, the unique identifier of the citing paper. Target, the unique identifier of the cited paper. Type, edges are ‘Directed’, and this column tells Gephi to regard all edges as such. Syr_date, this contains the date of publication of the citing paper. Tyr_date, this contains the date of publication of the cited paper. 3. 'OTSOC_Cited_2016-2021.csv' is a comma-separated values file that contain citations to all cited references that were cited by at least 10 of the retrieved papers within the OTSOC network published from 2016 onwards. The columns refer to:  Reference, the cited reference string extracted from the bibliographies of retrieved papers. Publication year, the publication year of the cited reference. DOI, the DOI of the cited reference.  indegree_2016, the total number of citations to a cited reference from papers published in 2016 and contained within the OTSOC network.  indegree_2017, the total number of citations to a cited reference from papers published in 2017 and contained within the OTSOC network.  indegree_2018, the total number of citations to a cited reference from papers published in 2018 and contained within the OTSOC network.  indegree_2019, the total number of citations to a cited reference from papers published in 2019 and contained within the OTSOC network.  indegree_2020, the total number of citations to a cited reference from papers published in 2020 and contained within the OTSOC network.  indegree_2021, the total number of citations to a cited reference from papers published in 2021 and contained within the OTSOC network.  total indegree 2016-21, the total number of citation to a cited reference from papers published between 2016-2021 and contained within the OTSOC network.  Software recommended for analysis Gephi version 0.9.2 was used for the visualisations within the manuscript, and both files can be read and into Gephi without modification. Notes <a href="#_ftnref1">[1]</a> Leng, G., Leng, R. I., Ludwig, M. (Submitted). Oxytocin – a social peptide? Deconstructing the evidence. <a href="#_ftnref2">[2]</a> Edinburgh University’s subscription to Web of Science covers the following databases: (i) Science Citation Index Expanded, 1900-present; (ii) Social Sciences Citation Index, 1900-present; (iii) Arts & Humanities Citation Index, 1975-present; (iv) Conference Proceedings Citation Index- Science, 1990-present; (v) Conference Proceedings Citation Index- Social Science & Humanities, 1990-present; (vi) Book Citation Index– Science, 2005-present; (vii) Book Citation Index– Social Sciences & Humanities, 2005-present; (viii) Emerging Sources Citation Index, 2015-present. <a href="#_ftnref3">[3]</a> For those interested, the following PMIDs were identified as ‘articles’ by WoS, but as ‘reviews’ by PubMed: ‘34502097’ ‘33400920’ ‘32060678’ ‘31925983’ ‘31734142’ ‘30496762’ ‘30253045’ ‘29660735’ ‘29518698’ ‘29065361’ ‘29048602’ ‘28867943’ ‘28586471’ ‘28301323’ ‘27974283’ ‘27626613’ ‘27603523’ ‘27603327’ ‘27513442’ ‘27273834’ ‘27071789’ ‘26940141’ ‘26932552’ ‘26895254’ ‘26869847’ ‘26788924’ ‘26581735’ ‘26548910’ ‘26317636’ ‘26121678’ ‘26094200’ ‘25997760’ ‘25631363’ ‘25526824’ ‘25446893’ ‘25153535’ ‘25092245’ ‘25086828’ ‘24946432’ ‘24637261’ ‘24588761’ ‘24508579’ ‘24486356’ ‘24462936’ ‘24239932’ ‘24239931’ ‘24231551’ ‘24216134’ ‘23955310’ ‘23856187’ ‘23686025’ ‘23589638’ ‘23575742’ ‘23469841’ ‘23055480’ ‘22981649’ ‘22406388’ ‘22373652’ ‘22141469’ ‘21960250’ ‘21881219’ ‘21802859’ ‘21714746’ ‘21618004’ ‘21150165’ ‘20435805’ ‘20173685’ ‘19840865’ ‘19546570’ ‘19309413’ ‘15288368’ ‘12359512’ ‘9401603’ ‘9213136’ ‘7630585’ <a href="#_ftnref4">[4]</a> Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a> <a href="#_ftnref5">[5]</a> Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. Gephi is available via <a href="https://gephi.org/">https://gephi.org/</a> <a href="#_ftnref6">[6]</a> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. <a href="https://doi.org/10.1038/s41598-019-41695-z">https://doi.org/10.1038/s41598-019-41695-z</a>

opencc-by-4.0Oct 2021View details →

zenodo48/100

Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK

Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"

opencc-by-4.0Oct 2024View details →

zenodo48/100

The POPREBEL semantic social network data

The <a href="https://populism-europe.com/poprebel/">POPREBEL project</a> explores the phenomenon of populism in Europe. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. It consists of coded interviews, realized between spring 2021 and spring 2022, to Internet users in Czechia, Germany and Poland, who used social media to gather information about the COVID-19 pandemic. The dataset is pseudonymized. POPREBEL is supported by the European Union's Horizon 2020 programme, grant n. 822682. <ul> <li><a href="https://zenodo.org/record/7494327">Final ethnographic report.</a> Section 1.2 contains a detailed description of how and why data were collected.</li> <li><a href="https://wellbeing.edgeryders.eu">Funnel website</a> of the project.</li> <li><a href="https://hal.archives-ouvertes.fr/hal-02478720/document">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>

opencc-by-4.0Dec 2022View details →

zenodo48/100

The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing

The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.

opencc-by-4.0Jul 2023View details →

zenodo44/100

Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project

This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files:  <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul>

opencc-by-4.0Jan 2020View details →

zenodo44/100

Internet use: participating in social networks [percentage of individuals] processed Eurostat data [CEEMID indicator]

The indicator 'Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]' from the Eurostat statistical product Individuals who used the internet, frequency of use and activities. - NUTS2013 regional codes are recoded to NUTS2016 - missing data is handled with last observation carry forward, next observation carry back, linear interpolation -NUTS2 areas are imputed when only NUTS1 level data is available.  The original dataset is available here: <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en">https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en</a> More about CEEMID: <a href="http://ceemid.eu">www.ceemid.eu</a> Get in touch: <a href="http://danielantal.eu/#contact">danielantal.eu/#contact</a>

opencc-by-4.0Apr 2020View details →

zenodo44/100

Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school

ABSTRACT The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals. INTRODUCTION Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies. The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018). Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019). These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks. PARTICIPANTS The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%). DATE STRUCTURE AND ARCHIVES FORMAT The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables. Social network The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation. The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables. In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated. Personal networks Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well). Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation. Sense of community and metropolitan displacements The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:      • Socio-economic data.      • Data on habitual residence.      • Information on intercity journeys.      • Identity and sense of community.      • Personal network indicators.      • Social network indicators. DATA ACCESS Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs. The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: <<a href="https://www.flickr.com/photos/25906481@N07/albums/72157667029974755">https://www.flickr.com/photos/25906481@N07/albums/72157667029974755</a>>. In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference: <ul> <li>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. <a href="https://doi.org/10.11144/Javeriana.upsy17-1.eerp">https://doi.org/10.11144/Javeriana.upsy17-1.eerp</a> </li> </ul> The English version of this article can be downloaded from: <a href="https://tinyurl.com/yy9s2byl">https://tinyurl.com/yy9s2byl</a> CONCLUSION The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed. The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences. ACKNOWLEDGEMENTS The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford. REFERENCES Brandes, U., & Wagner, D. (2004). Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), Graph Drawing Software (pp. 321-340). New York: Springer-Verlag.  Maya-Jariego, I. (2018). Why name generators with a fixed number of alters may be a pragmatic option for personal network analysis. American Journal of Community Psychology, 62(1-2), 233-238. DOI 10.1002/ajcp.12271 Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp Maya-Jariego, I., Lubbers, M. J. & Molina, J. L. (2019). A friendship network in decay: The dynamics of social relationships of a secondary school cohort over the transition to university. Remitido. Seidman, E., & French, S. E. (2004). Developmental trajectories and ecological transitions: A two-step procedure to aid in the choice of prevention and promotion interventions. Development and Psychopathology, 16(4), 1141-1159. https://doi.org/10.1017/s0954579404040179

opencc-by-4.0May 2020View details →

zenodo44/100

Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals. INTRODUCTION Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context.

opencc-by-4.0Nov 2020View details →

zenodo44/100

Social networks predict the life and death of honey bees - Data

Interaction matrices and metadata used in "Social networks predict the life and death of honey bees" <a href="https://www.biorxiv.org/content/10.1101/2020.05.06.076943v2">Preprint: Social networks predict the life and death of honey bees</a> See the README file in <a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition</a> for example code. The following files are included: interaction_networks_20160729to20160827.h5 The social interaction networks as a dense tensor and metadata. Keys: <ul> <li>interactions: Tensor of shape (29, 2010, 2010, 9) (days x individuals x individuals x interaction_types). I_{d,i,j,t} = log(1 + x), where x is the number of interactions of type t between individuals i and j at recording day d. See the methods section of paper of the interaction types.</li> <li>labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.</li> <li>bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual</li> </ul> alive_bees_bayesian.csv This file contains the results of the bayesian lifetime model with one row for each bee. Columns: <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>days_alive: Number of bees the bees was determined to be alive. If the individual was still alive at the end of the recording, the number of days from the day she hatched until the end of the recording.</li> <li>death_observed: Boolean indicator whether the death occurred during the recording period.</li> <li>annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.</li> <li>inferred_death_date: The death date as determined by the model.</li> </ul> bee_daily_data.csv This file contains one row per bee per day that she was alive for the focal period. Columns: <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>date: Date in year-month-day format.</li> <li>age: Age in days. Can be NaN if the bee has no associated death_date.</li> <li>network_age, network_age_1, network_age_2: The first three dimensions of network age.</li> <li>dance_floor, honey_storage, near_exit, brood_area_total: Normalized (sum to 1). Can be NaN if a bee had no high confidence detections (>0.9) for a given day. Can be 0 if a bee was only seen outside of the annotated areas.</li> <li>location_descriptor_count: The number of minutes the bee was seen in one of the location labels during that day. I.e., dance_floor * location_descriptor_count calculates the number of minutes, the bee was seen on the dance floor on the given day.</li> <li>death_date: Date the bee was last seen in the colony in year-month-day format. Can be NaN for individuals that did not die until the end of the recording period.</li> <li>circadian_rhythm: R² value of a sine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</li> <li>velocity_peak_time: Phase of the circadian sine fit in hours as an offset to 12:00 UTC. Can be NaN if circadian_rhythm is NaN.</li> <li>velocity_day, velocity_night: Mean velocity of the individual between 09:00-18:00 UTC and 21:00-06:00 UTC, respectively. Can be NaN if no velocity data was available for that interval.</li> <li>days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.</li> </ul> location_data.csv This file contains subsampled position information for all bees during the focal period. The data contains one row for every individual for every minute of the recording if that individual was seen at least once during that minute with a tag confidence of at least 0.9. The first matching detection for each individual is used. Columns: In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns: <ul> <li>cam_id, cams: The cam_id is a numerical identifier from {0, 1, 2, 3}. Each side of the hive is filmed by two cameras where {0, 1} and {2, 3} record the same side respectively. The cams column contains values either “(0, 1)” or “(2, 3)” and indicates to which sides of the hive this detection belongs.</li> <li>x_pos_hive, y_pos_hive: The spatial positions in millimeters on the hive. The two cameras from one side share a common coordinate system.</li> <li>location: The label that was assigned to the comb at (x_pos_hive, y_pos_hive) on the given date. The label “other” indicates detections that were outside of any annotated region. The label “not_comb” indicates the wooden frame or empty space around the comb.</li> <li>timestamp, date: The timestamp indicates the beginning of each one-minute sampling interval and is given in UTC, as indicated (example: “2016-08-13 00:00:00+00:00”). The date part of the timestamp is repeated in the “date” column. Both are given in year-month-day format.</li> </ul> Software used to acquire and analyze the data: <ul> <li><a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition: Network age calculation and regression analyses</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline/releases/tag/2016">bb_pipeline: Tag localization and decoding pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline_models/releases/tag/2016">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_binary/releases/tag/2016">bb_binary: Raw detection data storage format</a></li> <li><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></li> <li><a href="https://github.com/BioroboticsLab/bb_imgacquisition/releases/tag/2016">bb_imgacquisition: Recording and network storage </a></li> <li><a href="https://github.com/BioroboticsLab/bb_behavior/releases/tag/2016">bb_behavior: Database interaction and data (pre)processing, velocity calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_circadian/releases/tag/2016">bb_circadian: Circadian rhythm calculations</a></li> <li><a href="https://github.com/BioroboticsLab/bb_tracking_2016/releases/tag/2016">bb_tracking: Tracking of bee detections over time</a></li> <li><a href="https://github.com/BioroboticsLab/bb_wdd/releases/tag/2016">bb_wdd: Automatic detection and decoding of honey bee waggle dances</a></li> <li><a href="https://github.com/BioroboticsLab/bb_interval_determination/releases/tag/2016">bb_interval_determination: Homography calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_stitcher/releases/tag/2016">bb_stitcher: Image stitching</a></li> </ul>

opencc-by-4.0Jan 2021View details →

zenodo44/100

Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities

This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research. <h3>Dataset</h3> The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.   <table> <tbody> <tr> <td>Subreddit</td> <td>Posts</td> <td>Comments</td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table>   <h3>Annotation proccess</h3> The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as Toxic, Non-toxic, I do not know and Missing info. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing.  <h3>Available data</h3> The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below. - id: The unique identifier of the comment on the Reddit platform - body: The original comment text publication - is_toxic: The final label of a given comment. The label is 0 for non-toxic comments, 1 for toxic comments and -1 for comments where the raters disagreed about the toxicity.

opencc-by-4.0Dec 2023View details →

zenodo44/100

Plant metabolites modulate animal social networks and lifespan

Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, Athalia rosae. Adult sawflies acquire neo-clerodane diterpenoids ('clerodanoids') from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions in sawfly groups where some or all individuals had prior access to clerodanoids. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.

opencc-by-4.0Apr 2024View details →

zenodo44/100

Social Network Online Activity of 100+ Users Over Two Years

This dataset contains a precise (error margin is within 5 seconds) activity log of 138 users recorded over a period of approximately two years. It includes users' log in/log off timestamps as well as a device id which was used during the session. An activity heat map is also provided which can be used to determine the online time (in seconds) in a given hour for a given user. The dataset is completely anonymized and is not linked to real peoples' accounts. Russian social network VK was used to record the data. The database is provided in SQLite3 format. The data format is the following: 'sessions' table: <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>platform</td> <td>INTEGER</td> <td>Device identifier for the session (refer to the table below).</td> </tr> <tr> <td>time_from</td> <td>DATE</td> <td>Timestamp of the session's start.</td> </tr> <tr> <td>time_to</td> <td>DATE</td> <td>Timestamp of the session's end.</td> </tr> </tbody> </table> 'map' table: <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>hour</td> <td>INTEGER</td> <td>Hour from the 1st Jan 1970 (Unix Epoch / 3600).</td> </tr> <tr> <td>time</td> <td>INTEGER</td> <td>Accumulated online time in the hour (in seconds).</td> </tr> </tbody> </table> Device identifiers: <table> <tbody> <tr> <td>0</td> <td>Unknown</td> </tr> <tr> <td>1</td> <td>Web on Mobile </td> </tr> <tr> <td>2</td> <td>iPhone App</td> </tr> <tr> <td>3</td> <td>iPad App</td> </tr> <tr> <td>4</td> <td>Android App</td> </tr> <tr> <td>5</td> <td>Windows Phone App</td> </tr> <tr> <td>6</td> <td>Windows App</td> </tr> <tr> <td>7</td> <td>Web on Desktop</td> </tr> </tbody> </table>   This dataset is associated with the VKWatcher independent research project. The code used to gather the information can be found <a href="https://github.com/Azarattum/VKWatcher-Backend">on GitHub</a>.

opencc-by-4.0Nov 2021View details →

zenodo44/100

Dataset: Multi-level network dataset of social-ecological interdependencies in ten Swiss wetlands based on qualitative interviews and quantitative surveys

The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).

opencc-by-4.0Jul 2022View details →

zenodo44/100

Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors

This is the dataset and stata code for the paper "Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors” submitted to Plos One May 1st, 2024.

opencc-by-4.0Apr 2024View details →

zenodo44/100

Annotated Data in Spanish for Toxicity and Insults in Digital Social Networks

This repository contains data sets and materials for a gold standard elaboration on toxicity and incivility in the digital sphere based on human coding to benchmark algorithmic classification tasks with transformers and LLMs. The labelling progress is 62%. We are labelling two samples of novel datasets of political digital interactions on Twitter (rebranded as X). The first set comprises almost 5 million data points from three Latin American protest events: (a) protests against the coronavirus and judicial reform measures in Argentina during August 2020; (b) protests against education budget cuts in Brazil in May 2019; and (c) the social outburst in Chile stemming from protests against the underground fare hike in October 2019. We are focusing on interactions in Spanish to elaborate a gold standard for digital interactions in this language, therefore, we prioritise Argentinian and Chilean data. The second set contains more than 31 million messages and more than 9 million interactions between 2010 and 2022, covering the election of members of the first Constitutional Convention in Chile, the drafting process and the referendum in which the proposal was rejected. This project is generously funded by the OpenAI Academic Programme, 2024 FAE-UDP Research Grant, and partially by the St Hilda's College Muriel Wise Fund at the University of Oxford. The <a href="https://training-datalab.com/">Training Data Lab</a> research group also logistically supports this project.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Star Wars social network

Star Wars social network This dataset contains the social network of Star Wars characters extracted from movie scripts. In short, two characters are connected if they speak together within the same scene. The data contain characters and links from episodes I to VII. How the data were created is described in my blog posts: <ul> <li><a href="http://evelinag.com/blog/2015/12-15-star-wars-social-network/index.html">The Star Wars social network</a></li> <li><a href="http://evelinag.com/blog/2016/01-25-social-network-force-awakens/index.html">Star Wars social network: Force Awakens</a></li> </ul> The associated code is available in the main Github repository <a href="https://github.com/evelinag/StarWars-social-network">evelinag/StarWars-social-network</a>. Contents of the files are the following: <ul> <li> <code>starwars-episode-N-interactions.json</code> contains the social network extracted from Episode N, where the links between characters are defined by the times the characters speak within the same scene. </li> <li> <code>starwars-episode-N-mentions.json</code> contains the social network extracted from Episode N, where the links between characters are defined by the times the characters are mentioned within the same scene. </li> <li> <code>starwars-episode-N-interactions-allCharacters.json</code> is the <code>interactions</code> network with R2-D2 and Chewbacca added in using data from <code>mentions</code> network. </li> <li> <code>starwars-full-...</code> contain the corresponding social networks for the whole set of 6 episodes. </li> </ul> Description of networks The json files representing the networks contain the following information: Nodes The nodes contain the following fields: <ul> <li>name: Name of the character</li> <li>value: Number of scenes the character appeared in</li> <li>colour: Colour in the visualization</li> </ul> Links Links represent connections between characters. The link information corresponds to: <ul> <li>source: zero-based index of the character that is one end of the link, the order of nodes is the order in which they are listed in the “nodes” element</li> <li>target: zero-based index of the character that is the the other end of the link.</li> <li>value: Number of scenes where the “source character” and “target character” of the link appeared together. Please not that the network is undirected. Which character represents the source and the target is arbitrary, they correspond only to two ends of the link.</li> </ul>

opencc-by-3.0Jan 2016View details →

zenodo44/100

TwitCID: a Collection of Data Sets for Studies on Information Diffusion on Social Networks

The TwitCID collection consists of five Twitter datasets which were extracted from the 1 percent of tweets from Twitter API.  The Firstweek and Secondweek data set were collected during the first week and second week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords “iphone”, “gucci” and “galaxys” respectively.   We publish these datasets on behalf of our academic institution – IRIT, France and for the sole purpose of non-commercial research under the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike). In accordance with Twitter's Terms of Service, we only provide identifiers of tweets. In order to collect the actual tweets in JSON, you could use the script Collect_JSONtweets.py attached. If you would like to use this collection, please cite our paper:  Hoang, T. B. N., Mothe, J., & Baillon, M. (2019, September). TwitCID: a collection of data sets for studies on information diffusion on social networks. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 88-100). Springer, Cham.

opencc-by-4.0Jun 2019View details →

zenodo44/100

Social networks and transformative behaviors in a grassland social-ecological system

Dataframe for analysis presented in Nesbitt et al.'s Social networks and transformative behaviors in a grassland social-ecological system published in People and Nature. Dataframe includes responses from an ego network survey administered to Nebraska (USA) ranchers in 2021.  Metadata describes each variable in further detail including the question number from the survey.

opencc-by-4.0Jun 2024View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record