Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"

<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>

opencc-by-4.0Dec 2021View details →

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis during the COVID-19 pandemic (01.2020-06.2020)

Sources:  <ul> <li>Euractiv</li> <li>The Conversation</li> <li>Politico Europe </li> <li>IEEE Spectrum </li> <li>Techforge </li> <li>Fastcompany </li> <li>The Guardian (Tech) </li> <li>Arstechnica </li> <li>Reuters </li> <li>Gizmodo </li> <li>ZDNet </li> <li>The Register </li> <li>The Verge </li> <li>TechCrunch </li> </ul>   Methodology The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The analysed paragraphs are selected the following way: <ul> <li>The articles containing the given social issue are identified</li> <li>The paragraphs containing the social issue are selected for sentiment analysis</li> </ul> *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

opencc-by-4.0Sep 2022View details →

Young people's media use and adherence to preventive measures in the "infodemic": Is it masked by political ideology?

Data to replicate the publication "Young people's media use and adherence to preventive measures in the “infodemic”: Is it masked by political ideology?". This publication examines the role of political ideology and political extremism for COVID-19 information seeking and preventive behaviour with data of the COVIDisc project. COVIDisc investigates how young people aged 15 to 34 years perceive the discussion in the Coronavirus Pandemic, which messages reach them, what media they use to inform themselves and how they experience the situation. en

opencc-by-4.0Apr 2024View details →

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

opencc-by-4.0Dec 2023View details →

Selective Oxidation of 5-Hydroxymethylfurfural to 2,5-Diformylfuran in Biphasic Media using Immobilized Galactose Oxidase: Proof of Concept and Limitations

The oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-diformylfuran (DFF) is a key reaction in valorizing biomass. DFF is hardly soluble in water, while HMF is often obtained from biorefineries in crude wet organic fractions. Thus, the reaction is challenging for both biocatalysis performed in aqueous media, and for chemocatalysis where the presence of water often results in catalyst poisoning. Galactose oxidase (GalOx) can selectively oxidize HMF to DFF and displays promising activity in aqueous-organic media. In this study, GalOx was immobilized on ten carriers, assessing the immobilization yield, activity, and stability. Covalently immobilized GalOx catalyzed the oxidation of HMF to DFF in neat and water-saturated EtOAc, and in biphasic systems of various water contents. At 50 % v/v H2O, the reaction was conducted at a semi-preparative scale (50 mL) with no adverse effect on DFF yield. Some limitations arise, such as enzyme deactivation, and adsorption of DFF to the support, particularly in the aqueous fraction. Future options to upgrade the route may include designed stable enzymes under the presence of HMF/DFF, and the setup of microaqueous systems where DFF adsorption is minimized. The use of wet EtOAc media would be a promising approach in future biorefineries employing inexpensive crude wet organic fractions.

opencc-by-4.0Mar 2019View details →

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis Sources: Above 140k articles (01.2016-03.2019): <ul> <li>Gigaom 0.5%</li> <li>Euractiv 0.9%</li> <li>The Conversation 1.3%</li> <li>Politico Europe 1.3%</li> <li>IEEE Spectrum 1.8%</li> <li>Techforge 4.3%</li> <li>Fastcompany 4.5%</li> <li>The Guardian (Tech) 9.2%</li> <li>Arstechnica 10.0%</li> <li>Reuters 11%</li> <li>Gizmodo 17.5%</li> <li>ZDNet 18.3%</li> <li>The Register 19.5%</li> </ul> Methodology The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way: <ul> <li>The articles containing the given social issue are identified</li> <li>The paragraphs containing the social issue are selected for sentiment analysis</li> </ul> *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.   Files sentiments_mod11.csv sentiment score based on chosen unigrams sentiments_mod22.csv sentiment score based on chosen bigrams sentiments_cooc_mod11.csv, sentiments_cooc_mod12.csv, sentiments_cooc_mod21.csv, sentiments_cooc_mod22.csv combinations of co-occurrences: unigrams-unigrams, unigrams-bigrams, bigrams-unigrams, bigrams-bigrams

opencc-by-4.0Jan 2023View details →

[Raw data] Media Coverage of 3D Visual Tools Used in Urban Participatory Planning

Raw information on the articles used for the publication: Media Coverage of 3D Visual Tools Used in Urban Participatory Planning

opencc-by-4.0Jul 2020View details →

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-12.2019)

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis Sources with weights: <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> Methodology The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.   *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

opencc-by-4.0Aug 2023View details →

Predators of Japanese myriapods — survey using literature, social media, Web, and mobile application —

I provide a database reviewing information on predators of Japanese myriapods. The information within this database was compiled from published accounts in Japan along with visual media accumulated on the Internet.

opencc-by-4.0Dec 2021View details →

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2021)

opencc-by-4.0Dec 2018View details →

PERCEIVE The use of social media in EU policy communication and implications for the emergence of a European public sphere

This data set contains the underlying data of the paper “The use of social media in EU policy communication and implications for the emergence of a European public sphere”, published by The Journal of Regional Research - Investigaciones Regionales (ISSN: 1695-7253; E-ISSN: 2340-2717) in 2019. 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: <ul> <li>Data set: “PERCEIVE: WP3: Effectiveness of communication strategies of EU projects” <a href="https://doi.org/10.5281/zenodo.3371133">https://doi.org/10.5281/zenodo.3371133</a></li> <li>Data set: “PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels” <a href="https://doi.org/10.5281/zenodo.3371174">https://doi.org/10.5281/zenodo.3371174</a></li> </ul> 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.

opencc-by-4.0Dec 2022View details →

Data for the paper, "Social media data for environmental sustainability: a critical review of opportunities, threats and ethical use"

These data were collected for the paper, "Social media data for environmental sustainability: a critical review of opportunities, threats and ethical use" published in One Earth. It includes a database of studies applying social media data in environmental sustainability research, which were collected and reviewed in full by the authors. Rather than providing a comprehensive summary of all relevant literature like in a systematic review, our objective was to take stock and evaluate the previous body of work in the field in order to promote conceptual innovation from its critical examination. Building on a set of 169 studies collected in a previous systematic review of social media data applications in environmental research (Ghermandi and Sinclair 2019), the database includes additional relevant studies that were identified by snowballing previous references and adding further gray and scientific academic articles known to the authors. For studies to be included in our analysis, they had to involve the use of data from one or more social media platforms and investigate human interactions with and/or impacts on the environment. We relied on a broad definition of social media including any website or application that enables users to create and share content or to participate in social networking (e.g., blogging sites, recommendation sites, and online forums). We further strengthened the analysis by including insights from additional literature on social media that do not have a direct application to environmental sustainability (e.g., studies on biases in social media data). The final database consists of 415 studies, which were published between 2011 and 2021.   Ghermandi, Andrea, and Michael Sinclair. "Passive crowdsourcing of social media in environmental research: A systematic map." Global environmental change 55 (2019): 36-47.

opencc-by-4.0Dec 2019View details →

"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17

opencc-zeroAug 2020View details →

Hybrid nanoplasmonic porous biomaterial scaffold for liquid biopsy diagnostics using extracellular vesicles: data and media

Raw datasets and media accompanying the manuscript: Hybrid nanoplasmonic porous biomaterial scaffold for liquid biopsy diagnostics using extracellular vesicles, published in ACS Sensors

opencc-by-4.0Oct 2023View details →

Dataset of paper "Comparison of radiant intensity in aqueous media using experimental and numerical simulation techniques"

Dataset of paper "Comparison of radiant intensity in aqueous media using experimental and numerical simulation techniques":<ul><li>Relative spectral intensity for each light source as recorded by ILT RAA4 spectroradiometer.</li><li>Material and optical properties of the quartz tube.</li><li>Wavelength specific refractive index and quantum yield in water.</li><li>Ray tracing data vs experimental data (FX-1 265).</li><li>Plot of peak intensity vs working distance (in air) and Comparison between radiometry and ray tracing in the presence of a quartz tube in front of the light source for FX-1 265.</li><li>Recorded peak intensity at multiple working distances using radiometry and ray tracing.</li><li>Comparison between average light loss at multiple distances in radiometry, ray tracing and transmission curve due to quartz material and Plot of percentage light loss for all wavelengths at multiple working distances due to quartz material.</li><li>Data on loss of intensity in comparison with the transmission curve.</li><li>Recorded peak intensity at multiple working distances between simulation tools.</li><li>Comparison between ray tracing, DOM and radiometry for FX-1 265, Uniformity plot obtained from radiometry, ray tracing interface and DOM.</li><li>Data on comparison between actinometry measurements and ray tracing.</li><li>Plot of intensity with wavelength between the two techniques.</li><li>Comparison between measured intensity in air and water medium for FX-1 265.</li><li>Plot of change in peak intensity as light propagates through the quartz tube within the UV fixture at multiple working distances for FX-1 265.</li><li>Simulated intensity in water at different points within the tube.</li></ul>

opencc-by-4.0Nov 2023View details →

Descriptive Study on Nursing Students' Interest in Using Digital Communication and Social Media in their Field after Covid-19

This paper is about Descriptive study of nursing students' interest in using Digital communication in their field after Covid 19. Digital communication and social media have massive social interaction among community users that generate an extensive amount of data. The study was conducted in the faculty of nursing at Najran University, King Dome of Saudi Arabia (KSA). A descriptive correlational research design was followed in this study Sample: 300 nursing students. The first tool: is the interview sheet. Include two parts: Part (1) student personal data. Part (2) the nursing students' interest in Digital communication and social media. The second tool: - a questionnaire to Measure nursing students' knowledge about using Digital communication and social media in their field. Results: (74.7%) of nursing students were interested in Digital communication, and more than half (55.3 %) knew about Digital communication and social media, there's a statistical association between the academic level and students' interest. Provide special education about Digital communication to improve students' knowledge in the health field for using after covid 19, In-service Training for Various Digital communication and social media platforms. dissemination of the findings of this research study to the public and private healthcare organizations

opencc-by-4.0Sep 2017View details →

Dataset used for the article Intergroup Ethnocentrism and Social Media: Evidence from three western Democracies

This is the dataset, used for the paper Intergroup Ethnocentrism and Social Media: Evidence from three western Democracies, submitted to the Information, Communication & Society for review. This work was supported by an Audencia Foundation research grant Innovation and Stability.