dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, one avoids the security risk involved in directly providing copies of sensitive data to non-collaborator participants, e. g. , undergraduate or temporary research assistants; -It does not try to replicate all annotation software features, but is instead interoperable, which allows the user to use a specialized program for specific tasks (such as Praat, Boersma, 2009, for annotations that benefit from a spectrogram); -It keeps track of changes made to the annotations (using git, Git, 2018); -It is natively connected with a set of automatic analysis routines which may be useful later on, such as systems that automatically align matched audio and transcription files ("forced alignment"; WebMAUS,  Kisler, Reichel, & Schiel, 2017). One disadvantage of Emu-SMDS is that it requires a significant initial time investment in learning how to use the system, with the biggest hurdle being the need to set up a local server.
When one of the authors contacted the current maintainers of Emu-SMDS, they provided tips to help with set-up, and also offered to set up the server for us in their local environment. For readers who intend to request this service from the Emu-SMDS team in order to avoid the hassle of setting up their own server, this is yet another reason to ensure that appropriate data sharing permissions are set up early on in the project (see Section 3. 2 for discussion). Future virtual database managers may also be able to "propose" samples to the human annotator on the basis of the regions or types of recorded data that the system is trying (but failing) to analyze automatically. This type of system, sometimes referred to as "human in the loop" or "interactive machine learning" (e. g. ,  Holzinger, 2016) , is not yet available.
Conclusions We set out to comprehensively review the topics worth considering before embarking on a project centered on LFSE recordings. An emergent theme in our review was the critical need for more (and more consistent) shared annotated data. Without it, we cannot hope for better tools for automated analysis. For now, the answers to many research questions will still require significant manual annotation, and it is therefore paramount for researchers to plan ahead for long-term sharing long before data collection begins. Overcoming many of these challenges is likely best achieved through the effort of research teams and communities who can collectively address the larger theoretical and practical issues relevant to LFSE recording research. We hope that we have facilitated this process with our coverage of relevant issues above, but we may have missed some topics.
Further, much of the information we have included is bound to become out-ofdate as technology continues to develop. We encourage readers to join the DARCLE network (darcle. org) to stay in the loop about the latest developments in LFSE recording research. DARCLE is focused on child language, but many of the methodological considerations are universal to those considering LFSE recording studies, and exchanges between researchers working on different participant groups could be mutually beneficial. Readers can post about their own findings in that and similar mailing lists. A repository of papers using LFSE recordings has also been made available by  Ganek (2018). Her paper repository is community-augmented, meaning that anyone can add their resources to the list when they are ready to share.
Funding Information This work was supported by an  NWO   Veni Innovational Research Scheme grant  ( 275-89-033 ) to MC and by a  TransAtlantic Platform  " Digging into Data" collaboration grant  ( ANR-16-DATA-0004   ACLEW: Analyzing Child Language Experiences Around The World ) and  Agence Nationale de la Recherche  ( ANR-14-CE30-0003   MechELex ,  ANR-17-EURE-0017 ) and  J. S. McDonnell Foundation  grant to AC. long-format speech environment (LFSE) recordings Art. 24, page 18 of 21 Speech Communication Association (INTERSPEECH 2017),  2098-2102. DOI: https://doi. org/10. 21437/Interspeech. 2017-1418Casillas, M. ,  Brown ,  P. ,  & Levinson , S. C. (2017). Casillas HomeBank Corpus. Retrieved from https://homebank. talkbank. org/access/Secure/Casillas. html. Casillas, M. ,  Brown ,  P. ,  & Levinson , S.
org/. Fausey, C. M. ,  & Mendoza , J. K. (2018). FauseyTrio-Public HomeBank Corpus. doi: 10. 21415/T56D7Q. Retrieved from https://homebank. talkbank. org/access/Public/FauseyTrio-Public. html. Frank, M. C. , Braginsky,  M. , Marchman , V. A. , & Yurovsky, D. (2019). Variability and Consistency in Early Language Learning: The WordBank Project. Retrieved from https://langcog. github. io/wordbankbook/May 2019. Ganek, H. (2018). LENA Studies Spreadsheet. September 2018. DOI: https://doi. org/10. 17605/OSF. IO/54FY7  Ganek, H. ,  & Eriks-Brophy , A. (2018a). Language ENvironment analysis (LENA) system investigation of day long recordings in children: A literature review. Journal of Communication Disorders,  72 ,  77-85. DOI: https://doi. org/10. 1016/j. jcomdis. 2017. 12. 005Ganek, H. V. ,  & Eriks-Brophy , A.
Quantitative Sample To assure heterogeneity and representativeness among the firms in the sample, 1,000 randomly selected firms from the tourism and hospitality industry in the Austrian Chamber of Commerce's database of owner-manager led firms  (Getz et al. , 2004) , the dominant form of Austrian tourism firms  (Doerflinger, Doerflinger, Gavac, & Vogl, 2013) , were invited via email to participate in a survey from September to November 2017. This selection guaranteed a key informant approach, i. e. the firms' key informants and most knowledgeable information sources were addressed, as being common practice in EO studies  (Lumpkin & Dess, 2001). The questionnaire was developed on validated scales from literature (see Table  3 ). As these scales were originally in English, they had to be translated to German by two academics for conducting the survey in Austria.
Measures Data for the quantitative study was measured using 5-point Likert-type scales (1=strongly disagree; 5=strongly agree). To measure EO, the three sub-dimension scales from  Eggers et al. (2013)  were used, who adapted the original  Covin and Slevin (1989)  scales to a small-firm context: Innovativeness was measured with a five-item scale, particularly focusing on innovation behavior. Proactiveness was assessed with a five-item scale, measuring how eager firms are to identify and take advantage of market opportunities. Risk-taking was evaluated with a four-item scale, which concentrates on measuring the perception and management of uncertainty and risk within a company. Networking was measured with the two-item scale used by  Hills and Hultman (2006) , which evaluates a company's information exchange with its personal and professional network.
Resource availability Are there any financial resources available that your firm could use immediately if you had to invest in your firm instantly? Table  4. Questions for follow-up study The interviews were all tape-recorded and transcribed. In a next step, the content of the interviews was structured according to the identified configurations from the quantitative study. As the original data were in German, relevant quotes illustrating the different configurations were independently translated into English by two of the authors to ensure accuracy and meaningful translations of quotes. Finally, the translations were compared, and a professional language editor was consulted to ensure precise wording. As well as helping to interpret the results of the quantitative study, the gathered information of the follow-up study also helped to name the six identified categories from the quantitative study.
Respect of first-order stochastic dominance. In order to assess accurately the performance of the task by the two monkeys, we consider separately the lottery pairs where there was a strictly dominant option (lottery pairs Type 1-5). The results of the 96 lottery pairs meeting this condition are summarized in Fig. 5  (Monkey H: 19594 trials; Monkey G: 16938 trials). In the supplementary section, Table  S3  provides the median success rate for each type of lottery pairs and Fig. S1  provides a similar representation of performances as Fig. 5 , but considering separately 20 chunks of data in order to control for the stability of performances over time. As the task design includes only two choice options, the chance level is 0. 5. Results show that all interquartile ranges of success rates are above 0.
Data used were obtained on 18 LPG and 18 LPL without stochastic dominant option (Monkey H: 4952 trials, Monkey G: 4435 trials). Results are shown in Fig. 6. In the supplementary section, Table  S4  summarizes the results of the optimization process. The slope of the curve of the sigmoid function in Fig. 6A ,B on the left side (in blue) is steep and the points are close to the curve, indicating that both monkeys consider the difference of expected values when realizing trade-offs between quantity and probability for lotteries with potential gains (Monkey H: β G = 1. 70 [0. 72, 2. 68], Monkey G: β G = 1. 83 [1. 55, 2. 12]). However, the slope of the curve of Fig. 6A ,B on the right side (in orange) is smoother and the points are further from the curve than for gains. This indicates that both monkeys less consider the difference of expected values when dealing with lotteries including potential losses, and we can not reject the hypothesis that they do not consider it at all (Monkey H: β L = 0.
• • λ G and λ L that describe to which extent the decision-making process is stochastic for lottery pairs involving respectively gains and losses. Reference values are 0 (maximal stochasticity) and ∞ (no stochasticity at all). We optimized these free parameters for each monkey separately (Monkey H: 4952 trials, Monkey G: 4435 trials). In order to control for the stability over time of the fit, we separated for each monkey the dataset in 20 chunks with respect to the chronological order of the trials, obtaining 20 best-fit parameter values for each parameter. For describing to which extent the two monkeys present asymmetry of treatment between gain and loss, we compared the best parameters values contrasting those computed after the fitting of results obtained with 18 LPG without stochastic dominant option, and those computed after the fitting of results obtained with 18 LPL whitout stochastic dominant option.
INTRODUCTION The present study focuses on the identification of the optimal driving profiles on tramway systems. To achieve this, an experimental campaign has been carried out to measure various parameters from an operational tramway system. Data for this study was collected via a dynamically re-configurable Information Communication Technology (ICT) infrastructure to facilitate both the operation and the end-user services supported by the railway network. Despite the recent progress in railway smart metering solutions and the implementation of several experimental trials, optimal operation of the ICT network supporting the power grid remains an unsolved challenge, even in the case where monitoring is limited to the rolling stock. The same holds for the interoperability between different heterogeneous network segments, which are currently static and unaware of each other.
g. optical) systems converging energy and telecom services. This infrastructure is used to interconnect a plethora of monitoring devices and end-users to the Operational Control Center (OCC). b) A platform that relies on a hybrid data storage and processing mechanism combining state-of-the art open source SQL/Non-SQL databases as well as batch and stream processing engines. Based on the characteristics of the collected data and the selected applications, data are dynamically forwarded to the most suitable storage/processing platform. A high-level view of this process is shown in Figure  1. Once information has been collected, the extracted knowledge is used to support a set of applications that can improve the energy efficient operation of railway systems. A typical example includes the identification of the optimal driving profiles in terms of energy consumption.
In the present study, this is achieved through the adoption of an optimization framework based on Data Envelopment Analysis (DEA). The performance of the proposed scheme is evaluated based on actual data collected from an operational tramway system. The rest of the paper is organized as follows. Section 2 outlines the objectives of the proposed study, Section 3 gives a brief overview of the state of the art on the subject, the research methodology along with a description of the proposed scheme is provided in Section 4. Finally, Section 5 concludes the paper.
The overall solution is shown in Figure  1. As mentioned above, this platform is used to monitor a variety of parameters. An indicative sample of the collected measurements is provided in Table  1. This dataset includes information related to the geographic location of the rolling stock, on-board CO2 levels that is used to estimate the number of passengers, internal and external temperature that is important for the evaluation of the Heating Ventilation and Airconditioning system's (HVAC) performance, kinematic parameters (including acceleration and speed) etc. The smart metering solution also comprises an Information Technology (IT) segment that is responsible for the storage and processing of the measurements. Storage is accommodated by hybrid mechanism combining state-of-the-art open source SQL/NoSQL databases while processing is executing based on Apache Spark.
The most efficient DMU is assigned an efficiency score of 1, and the performance of other DMUs vary between 0 and 1 relative to the best performance. To apply DEA in railway environments, driving styles are treated as DMUs. Now, let  be the set of driving styles extracted from the dataset with   ,  ∈ , being the vector of inputs of style  , with  elements   ,  ∈. Let   ,  ∈  be corresponding vector of outputs with size  (  = [ 1 ,  2 , … ,   ]. Let also  0 = [ 01 , …  0 ] be the inputs of the driving style that we want to evaluate and   = [ 01 ,. ,  0 ] the output vector. Introducing parameter λ  indicating the weight given to driving style  in its attempt to dominate Style 0, the measure of efficiency  of Style 0 is determined through the solution of the following optimization problem: 𝜆 𝑖 ≥ 0∀𝑖 ∈ 𝑆 Constraint (1) limits the inputs of all other driving styles below the inputs used by the reference model 0, while equation (  2 ) selects the driving styles that outperform style 0.
Answering these research questions is also important to understand when and how organizational members decide to disconnect from their mobile devices on and off the job. To address these two research questions, we analyzed a corpus of comments that LinkedIn users posted in reaction to an article discussing the risks for social relationships associated with the excessive use of mobile phones. Electronically-collected data, such as comments that are publicly available online or in virtual forums, are increasingly used in qualitative research for several reasons. First, they are deemed to be more credible and authentic than data collected through interviews or focus groups  (Pendry & Salvatore, 2015). Second, sharing sensitive information, such as information on when and how individuals decide to disconnect from work, is emotionally easier for users online than in face-to-face forum groups or traditional interview settings  (Smedley & Coulson, 2018).
Third, electronically-collected data presents the advantage of being collected over a longer time frame and of covering a larger pool of potential participants, which reduces the burden of participation and time pressure  (Im & Chee, 2006). Last, "online interaction can foster offline engagement", as demonstrated by  Pendry and Salvatore (2018, p. 211) , making such an approach promising for the current research. The LinkedIn article we examined in the present endeavor received 168,864 views, 2,928 likes and 975 comments over a period of more than 12 months from users located in several countries and employed in different roles and professions. We read all comments and focused our analysis on the 242 comments that specifically described disconnection decisions that users engaged in with the goal of regulating their smartphone use.
Data Data for this article were gathered by collecting and analyzing a corpus of comments published online on LinkedIn in response to an article posted on the same social network on February 5th, 2013. The article, entitled "The 21-day challenge -no phone in the company of others", described the risks related to excessive use of mobile phones. In the article, the author narrated her encounter with a taxi driver who was complaining about the lack of conversation with customers because of their incessant use of smartphones during rides. At the moment of data collection, the article had received more than 168,864 views, 2,928 likes, and 975 comments and had been shared 9,419 times by LinkedIn users, who described their personal experiences with information and communication technologies and decisions made to tackle this issue.
Among the 975 comments, some were simple praise of the article for raising the topic ("This is an excellent post!"); others were authored by enthusiastic followers who communicated their benefits from regulating the use of smartphones ("…my eyes hurt less at the end of the night"). From our reading, we noticed that 242 comments (24. 8%) describing specific disconnection decisions. These comments constitute our final dataset. Comments ranged from short sentences describing the individual decisions to disconnect and stop using smartphones at all (e. g. "I have deliberately avoided getting a smartphone at all") to longer and more detailed descriptions of actions taken to regulate their smartphone use both at work and at home. Using the LinkedIn account of one of the authors, we were also able to associate the available demographic information (i.
e. gender, job position, and industry) with each comment. The total number of words for the 242 comments in our final dataset was about 14,500. Gender was fairly evenly distributed, as 47% of the comments were authored by men and 51% by women, whereas 2% of the comments were anonymous. Respondents lived in numerous countries: given that the author of the LinkedIn article is American, many comments were authored by Americans, but others were written by people living in the UK, Germany, Italy, Saudi Arabia, India, and Australia. Commentators were representative of numerous industries, including marketing and sales (18. 5%), information technologies (17. 5%), telecommunication and internet services (6. 1%), counselling (5. 6%), education (5. 6%), consulting (5. 1%), publishing (5. 1%), human resources (4.
Data analysis The comments posted to this LinkedIn article are publicly viewable online i , so no particular action could have been taken to protect the privacy and confidentiality of the commenters. Numerous research ethics committees, such as the Norwegian National Committee for Research Ethics in the Social Sciences and the Humanities ii , indicate that researchers can use material from open forums without obtaining the consent from the parties, provided that they inform the company hosting the forum. Data were analyzed using Dedoose, an online software for qualitative data analysis, and independently coded by two researchers. Following indications by  Gioia and colleagues (2013) , data analysis consisted of two main phases: first, we read the comments to capture their meaning and coded them using the informants' language as much as possible (first-order codes); second, we iterated with the literature to analyze the informants' terms in more abstract and theoretical ways, discerned response patterns in the data, and grouped the comments into theoreticallyrelevant categories (second-order concepts/themes).
In the first stage of data analysis, we analyzed the comments to generate themes capturing (1) the motivations underlying the decisions to disconnect and (2) the actual decisions. This process generated hundreds of statements. Sample codes for motivations were "to increase situational awareness", "to avoid being interrupted", and "to avoid appearing rude", while sample codes for decisions were "turning mobile phone off", "ignoring it", and "keeping it away of the sight". In the second stage of data analysis, we paid particular attention to nascent patterns that could help to explain the phenomena observed and that were not referenced in the existing connectivity and boundary management literature. Using an iterative process of going back and forth across the data and the literature  (Alvesson & Kärreman, 2007; Miles & Huberman, 1994) , we looked for commonalities among the emerging themes to collapse the number of themes into "a more manageable number"  (Gioia et al.
M A T E R I A L The most frequent combinations of each tale type with others have been obtained empirically using the work of Uther, who only listed combinations that occur in at least three examples, those with at least eight examples being listed first. This makes it possible to minimise the risks associated with categorising versions: for instance, a version C misclassified as a tale type A combined with a tale type B is not enough to bias the connection between A, B and C for the entire tale type. It should be noted that non-European traditions are severely under-represented in this database (as Uther himself acknowledges) and that our results are only completely valid in Western Eurasia. To avoid sample bias, high (eight examples at least) and low levels (less than eight examples) of combination between two types have been analysed together and all nodes have been treated as identical: indeed, the boundary between seven and eight combinations depends on factors too random to be valid.
Network Construction The matrix of connected tale types was analysed using Social Network Visualizer v. 2. 3. (Kalamaras 2017; matrix: adjacency). Many centrality measures (Degree Centrality; Eigenvector Centrality; Betweeness Centrality) were used to address the question, "what is the most central ATU tale type in the studied network?" and to quantify the 'importance' or 'influence' of each ATU tale type. Each measure is based on different assumptions, rather than the classical two-dimensional presentation of the database. In order to make the results readable as widely as possible, two graphical representations showing the same results were always used. The ATU tale types corresponding to the numbers are given in Appendix 1. The Degree of Centrality (DC) is the sum of edges attached to a node, i.
e. in the current paper the number of direct relations of a tale type with others. DC is a standardised index (0 ≤ DC ≤ 1). An ATU tale type with a high score is often a node highly connected in the network, yet a large number of the relations could be with low level entities (Table  1 ; Figures  1a  and 1b ). The mean DC is 0. 042 (variance = 0. 001) with 23 classes. This measure is the most objectively representative of the database. The Eigenvector Centrality or EVC (see  Bonacich 1972 ; standardised index: 0 ≤ EVC'' < 1) is an extension of the simpler DC because it assigns a score proportional to the scores of its neighbours to all nodes. As for the DC method, EVC takes account the number of connections that every tale type has. With this method, high scoring nodes contribute more to the score than equal connections to low scoring nodes (Table  2; Figures 2a  and 2b ).
As a result, the average shortest path length (or number of mediating edges) between two ATU tale types calculated from the whole dataset (3. 38774) increases between 26. 16 and 29. 92% (DC: 4. 35568, +28. 57%; EVC: 4. 40144, +29. 92%; BC: 4. 27397, +26. 16%). According to these results, the ATU tale types with the highest scores represent points of failure in the network, their deletion leading to a dramatically increased average path length.