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opencc-by-4.0Dec 2017View details →

Worskshop: a Neurobioethical Perspective on Informed Consent (4 videos)

UNESCO Chair presents is first one-day workshop: A Neurobioethical Perspective on Informed Consent  within the <a href="http://i-consentproject.eu/">i-Consent</a> project which works on improving guidelines for Informed Consent, including vulnerable populations, under a gender perspective. During the workshop ten experts on the field will discuss: 1. What would be the consequences of reaching an identification of the neural correlates of the ordinary decision-making process behind the notion of informed consent? 2. Cognitive and other enhancers might paradoxically create new groups of vulnerable populations (i.e. soldiers): what are the implications of this awareness in relation to informed consent? 3. Cultural biases have been proven to exist also at a neuronal level, in which way should we filter such an information in relation to informed consent?

opencc-by-4.0Sep 2021View details →

Relations from Italian Wikipedia using Unsupervised Information Extraction

This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework. WikiOIE is based on UDPipe and the Universal Dependencies project for text processing. It easily allows customizing the information extraction (IE) approach to automatically extract triples (subject, predicate, object). This dataset contains relations extracted by two unsupervised IE methods. The former (simple) is based only on PoS-tag patterns; the latter (simpledep) also uses syntactic dependencies.  The extraction process is provided in JSON format. More information and the Java code are available here https://github.com/pippokill/WikiOIE Pierluigi Cassotti, Lucia Siciliani, Pierpaolo Basile,Marco de Gemmis, and Pasquale Lops. 2021. Extracting relations from Italian Wikipedia using unsupervised information extraction. In Proceedings of the 11th Italian Information Retrieval Workshop 2021 (IIR 2021). CEUR-WS.

opencc-by-4.0Sep 2021View details →

Analysis of the concept of informal economy through 102 definitions: legality or necessity

Abstract The processes of informal economy are well established, but the same cannot be said of their conceptual treatment in the academic literature. They constitute complex phenomena that cut across sectors and disciplines and give rise to other elements that simultaneously reject and encourage them. For many formal stakeholders in the economy, they are an enemy to be beaten; for the authorities, informal activity is seen as a loss of revenue for the state coffers; for the Sustainable Development Goals, by implicitly recognizing them in goal 8, they constitute a paradigm shift.  Meanwhile, the reality for those involved in the informal economy is that it is a way of life and not a mere choice, one that leads to the most social of all economies: that of necessity. There is no consensus among academics on informality and its ramifications, hence the need to analyze the processes of informal economy from its theoretical construction with the purpose of discovering its range and depth, as well as its interrelationships and theoretical implications. To achieve this, 102 definitions of informal economy were analyzed by identifying and deconstructing their dimensions and performing a frequency count of their citation in Google Scholar. This analysis demonstrated the lack of cultural elements in the definitions, which are the true underlying cause of the phenomenon, and the over-prominence afforded to legal dimensions. Plain language summary People have to find many different ways to earn a living in order to meet the needs of their families.  Some of these modes of employment, the so-called informal economy, clash directly with the wishes of governments and organizations who do not recognize that decent paid work may take many forms or do not understand that there are often no alternatives. This confused picture of contradictory opinions requires some clarity, which was the aim of this study in its analysis of the definition of informal economy by 102 experts. The analysis sought to reveal the meaning of this concept that is fundamental to life in society, especially in countries suffering from environmental challenges, political crises, or the scourge of corruption. The findings demonstrate the lack of cultural considerations in the academic study of this complex phenomenon, despite their inherent importance. If progress is to be made in improving working conditions, greater understanding is needed of how the most basic needs affect the way society functions.

Fig. 5 in First Record of Riccardoella (Proriccardoella) triodopsis (Acariformes: Trombidiformes: Ereynetidae) from Japan, with Additional Morphological Information

Fig. 5. Setae on tarsus and palptarsus and solenidion of Riccardoella (Proriccardoella) triodopsis. A–B: Setae (v) and (l) and solenidion ω on palptarsus (female, 21441E). C: Solennidion ω and setae fl″ζ on tarsus I (female, 21441B). D: Eupathidion p′ζ on tarsus II (female, 21441D). E–F: Famulus k″ and its guard seta l″ on tibia I (female, 21441E). Scale bars: A–C, 10 µm; D–F, 5 µm.

Fig. 1 in First Record of Riccardoella (Proriccardoella) triodopsis (Acariformes: Trombidiformes: Ereynetidae) from Japan, with Additional Morphological Information

Fig. 1. Idiosoma of Riccardoella (Proriccardoella) triodopsis (female, 21441A). A: Dorsal view, B: ventral view and coxae. Scale bar: 100 µm.

Fig. 3 in First Record of Riccardoella (Proriccardoella) triodopsis (Acariformes: Trombidiformes: Ereynetidae) from Japan, with Additional Morphological Information

Fig. 3. Legs of Riccardoella (Proriccardoella) triodopsis (antiaxial face). A: Left leg I (female, 21441E). B–D: Left leg II and right legs III and IV: (female, 21441B). Scale bar: 50 µm.

Fig. 4 in First Record of Riccardoella (Proriccardoella) triodopsis (Acariformes: Trombidiformes: Ereynetidae) from Japan, with Additional Morphological Information

Fig. 4. Lyrifissures and setae on idiosoma of Riccardoella (Proriccardoella) triodopsis. A–C: Lyrifissures ia and im (female, 21441A) and ih (female, 21441C). D: Shapes of setae on idiosoma and additional setae vi (arrow) (female, 21441D). Scale bars: A–C, 5 µm; D, 10 µm.

opencc-by-4.0Sep 2021View details →

supplementary information on the fauna from Tel Rehov

Supplement 1: List of archaeozoological assemblages with NISP > 100 from Iron Age II sites in Israel.  Supplement 2: Osteometric measurements in Tel Rehov.   Comparison_Fauna_Composition.pdf. (A) Correlation plot for animal groups (caprines, cattle, suids, game animals – deer and gazelle, transport animals – donkeys and camels) based on logit-transformed relative frequencies of the animals represented in 32 Iron Age II assemblages with NISP > 100 (10.5281/zenodo.5544926 , supplement 1). Numbers above the diagonal represent Pearson correlation coefficients, and asterisks denote statistical significance ( *, P < 0.1; **, P < 0.05; ***, P < 0.001). (B) Principle components analysis of the logit transformed taxonomic relative frequency data. Colors mark geographic location, plotted in (C); numbers refer to site identifiers in Supplement 1. Arrow angle with the axis indicate the contribution of each taxon’s frequency to the principle components, and transparency their overall importance. Statistics used the ‘factoextra’ package in R (4.02)(R Core Team, 2020).   Sheep_LSI.jpeg. Changes in the log-size ratio of sheep measurements, calculated using the ‘zoolog’ package (REF) in R (R Core Team 2020) from the measurements in 10.5281/zenodo.5544926 (supplement 2).

opencc-by-4.0Aug 2021View details →

MUHSIC: An Open Dataset with Temporal Musical Success Information

Music is a volatile industry, where its dynamic nature can directly influence artist career behavior. That is, musical careers can suffer ups and downs depending on the current market moment. This dataset provides data about hot streak periods in musical careers, which are defined by high-impact bursts occurring in sequence. Success in the music industry has a temporal structure, as the audience tastes change over time. Here, we use the Billboard Hot 100 charts with Spotify data to represent success over time. For musical careers, we build their time series from the debut date (i.e., date of the first release obtained from Spotify) to the last chart collected. Thus, each point in the time series represents the success of such an artist in a given week, according to the Hot 100 chart.  Therefore, we present MUHSIC (Music-oriented Hot Streak Information Collection), which contains: <ul> <li>Charts: enhanced data on all weekly Hot 100 Charts</li> <li>Artists: artist success time series with hot streak information</li> <li>Genres: genre success time series with hot streak information (the genre is the aggregated of all its artists)</li> <li>Hot Streaks: summarized hot streak information</li> </ul>

opencc-by-4.0Dec 2018View details →

FIFA World Cups 2010, 2014 and 2018 matches' information datasets

Data from FIFA World Cups 2010 (South Africa), 2014 (Brazil) and 2018 (Russia) collected from the FIFA website from the section of each match's facts. The original data were pdf files, which were cleaned and organized. The data was collected for the analysis of competition networks in soccer matches, where the high and low performance teams were studied. The zip file contains 3 folders of each world cup. Each folder contains the following files: - Players performance measures: input_worldcup.csv. This file contains information from 'fulltime statistics' and 'teams statistics' original files, complete for each match played in the world cup.  More detailed player statistics is only complete for the teams that were champion, runner-up, and the teams that accumulated the less points and goals-against in their respective world cup. - The description of each column can be found in data_dictionary_worldcup.csv. - The list of countries that participated in the world cup with its abbreviation is found in countries_abbreviations_worldcup.csv. - The result of each match is found in tags_worldcup.csv - The players position information is the tactical line-up for World Cup 2010 and initial actual formation for World Cups 2014 and 2018. This information was manually set as a best approximation of position for the starting 11 players and a receiving shots nodes 'XS' - special for the visualization developed. The x-axis is between 0.5 and 6 and y-axis is flipped and between 0 and 6. - Folder of ball passing distributions for each match and team. The name of each file contains the match number and the team's ball passing distribution. The column names are the players numbers who received the ball. Rows represent 'from' and the columns 'to'. Each value are the number of passes between players.

opencc-by-4.0Oct 2022View details →

Dataset for "Di-synaptic specificity of serial information flow for conditioned fear"

Dataset for the publication "Di-synaptic specificity of serial information flow for conditioned fear"

List of websites with CS projects information

The dataset contains the list of wesbites from where TIDE-UPF extracted the CS projects information.

opencc-by-4.0Jul 2022View details →

Socio-economic information and preferred interaction modalities related to eco-feedback systems

This dataset has been collected during a research conducted by the above authors to understand and improve upon the interactivity, functionality, and ultimately the sustainability effect of smart plugs. As observed in the spreadsheet, we collected socio-demographic information and gathered anonymous responses to three speculative scenarios related to how new eco-feedback systems are defined. Specifically, the dataset reports on the following aspects:   <ol> <li>Description and rationale of an eco-feedback information idea they would like to receive (what). This was provided via free-form text.</li> <li>Description of how a smart plug would display this information (how), provided via free-form text. This included one sub-question asking participants which Wattom features their scenario better relates to – provided via multiple choice using four short GIF animations of each feature in action.</li> <li>Description of which location would this information be most useful in (where). This was provided via three sub-questions: (i) four multiple choice options (home, work, shared home, other); (ii) a free-form text with more specific location information; and (iii) 23 multiple choice options with color-coded icons of various appliances and devices).</li> <li>Description of when this information should be displayed throughout the time (when). This was provided via five multiple choice options with the images illustrated in Figure 1.</li> </ol>

Supporting Information (software and data) for: Client-side energy and GHGs assessment of advertising and tracking in the news websites

This is the open data and free/libre and open source software repository for the article "Client-side energy and GHGs assessment of advertising and tracking in the news websites" by Fabio Pesari, Giovanni Lagioia, Annarita Paiano.

Reputation Communication from an Information Perspective

Here, the data underlying the article "Reputation Communication from an Information Perspective" is provided. There are two example simulations, one with 3 ordinary agents and one with a dominant agent among two ordinary agents. Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained: <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>

Data of: Cross-sectional survey on Germans' awareness for refugees' information barriers

The present dataset is the result of a cross-sectional online survey, which had been conducted to examine selected predictors of Germans' problem awareness in the form of perceived information barriers that refugees face, placing an emphasis on the role of positive intercultural contact experiences. The survey content is based on an extended version of the Empathy-Attitude-Action model and was carried out with a sample of Germans.  The dataset is in xlsx-format and the variable-descriptions can be found in the headings of the spreadsheet.

Supporting information for "Cosolvent effects on the structure and thermoresponse of a polymer brush: PNIPAM in DMSO-water mixtures"

This deposition contains the data and analysis (Jupyter notebooks) detailed in "Cosolvent effects on the structure and thermoresponse of a PNIPAM brush”. All Jupyter notebooks have also been converted into PDF files for ease of viewing. All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains three sub-directories: <ul> <li>FTIR <ul> <li>FTIR transmission data of binary DMSO-water mixtures as a function of solvent composition.</li> <li>FTIR deconvolution was performed using software readily available at <a href="https://github.com/haydenrob/spec_deconv">https://github.com/haydenrob/spec_deconv</a>.</li> </ul> </li> <li>Ellipsometry <ul> <li>Data directory containing all raw ellipsometry data.</li> <li>“refellips_Spectroscopic_SL.ipynb” notebooks to reproduce the analysis of a hydrated (solid-liquid) polymer brush. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips</a> repo.</li> <li>A spatial map of the polymer brush used for spectroscopic ellipsometry data analysis: “surface_map.png”.</li> <li>“Ellipsometry_logistical_fitting.ipynb” notebook and “DMSO_6mol_results.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.</li> <li>“refnx_dry.ipynb” and “refnx_solvent.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox</a> repo.</li> </ul> </li> </ul>

opencc-by-4.0Aug 2020View details →

Neural Networks for Structure-Informed Prediction of Formation Energy (employed in SIPFENN)

pySIPFENN Documentation: <a href="https://pysipfenn.org">pysipfenn.org</a> pySIPFENN GitHub: <a href="https://github.com/PhasesResearchLab/pySIPFENN">git.pysipfenn.org</a> Original SIPFENN Paper: <a href="https://doi.org/10.1016/j.commatsci.2022.111254">10.1016/j.commatsci.2022.111254</a>   Network Changelog: V 0.10 - All models moved to the open ONNX format for improved interchangeability; NN30 neural network similar to NN20 but accepting the new KS2022 feature vector; Python code migrated to public GitHub repository. V 0.9 - Python code updated to the release version; paper published V 0.8 - Python code (beta) to run models included V 0.7 - Original upload of development models    Selected works with SIPFENN alongside DFT and experiments: - <a href="https://doi.org/10.1016/j.actamat.2021.117448">10.1016/j.actamat.2021.117448</a> - <a href="https://doi.org/10.1038/s41598-021-03578-0">10.1038/s41598-021-03578-0</a>   SIPFENN Abstract (original publication, 2021): In recent years, numerous studies have employed machine learning (ML) techniques to enable orders of magnitude faster high-throughput materials discovery by augmentation of existing methods or as standalone tools. In this paper, we introduce a new neural network-based tool for the prediction of formation energies based on elemental and structural features of Voronoi-tessellated materials. We provide a self-contained overview of the ML techniques used. Of particular importance is the connection between the ML and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system.          In the course of this work, over 30 novel neural network architectures were designed and tested. This lead to three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve mean average error (MAE) of 28, 40, and 42 meV/atom respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8.          We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a user interface.    Contacts: - Adam Krajewski: ak@psu.edu - Prof. Zi-Kui Liu: zxl15@psu.edu

dryad40/100

Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations

The study associated with this dataset proposes a way of performing age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations. Here, you find simulation code in R to produce figures in the manuscript and matrices reporting demographic data upon which code computations are performed.

opencc-zeroNov 2022View details →

Information and Agreement in the Reputation Game Simulation

Here, the data underlying the article "Information and Agreement in the Reputation Game Simulation" is provided. There are 100 simulations with different random seeds to ensure statistically meaningful results. Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained: <ul> <li>first line <ul> <li>x_est: intrinsic honesties of the agents</li> <li>fr_affinities: intrinsic friendship affinity values of the agents</li> <li>shynesses: intrinsic shyness values of the agents</li> <li>perc_one_to_one: percentage of one-to-one conversations</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> <li>mode: strategy used by the special agent</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b_set: set of receivers. Can be either a single receiver or several</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>relationsc_<id>: number of conversations the updating agent has had with agent <id></li> <li>relationsm_<id>: number of messages the updating agent received about agent <id></li> <li>friendship+_to_<id>: the updating agent counts one friendly statement of agent <id>, i.e. the updating agent rates agent <id> now a little more as a friend</li> <li>friendship-_to_<id>: the updating agent counts one unfriendly statement of agent <id>, i.e. the updating agent rates agent <id> now a little more as an enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x/fr_affinity/shyness: the agent's intrinsic personality traits</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>relationsm/relationsc: number of messages (conversations) the agent heard about (had with) all others</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friendships: the agent's friendship status with all others, given as parameters of a beta function</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>