dataesr/scientific-paragraphs-categorization huggingface dataset:
This led to a fruitful discussion on transitioning from being an energy engineer to an energy data scientist in the industry, and what such a shift entails. Despite the largely positive reviews, there remain considerable differences in student experience. Almost 45% of the students in 2021 felt the course was either too basic or too advanced (split almost evenly between students who found the course a bit too advanced and those that found it a bit too basic). This number was about the same as the feedback we received in 2020. Based on the huge diversity in the cohort, this is expected but something we hope to work towards reducing in the future, possibly through optional preparatory lessons at the beginning of the course. Likewise, a considerable number of students felt there was either too much theory in the course (2021) or too little theory (2020).
However, while these were not explicitly discussed during the course, there were relevant readings on the topic; furthermore, the principles discussed in the class and the competition could easily generalize to cover this case. A case where this did not work out was for a student who attended the course hoping to learn about databases, and how to store and retrieve data from them. This topic was not covered, even tangentially, in the course. Based on discussions with students and alumni, the high levels of dropouts were caused due to a number of reasons. Foremost amongst these was the nature of the course, which lasted six weeks during the summer, was offered for free, and required substantial amounts of work. Without the carrot of academic credits, only highly selfmotivated students who had uninterrupted availability for six weeks managed to finish the course.
Focus on Real Contexts, Hands-On Practice and Instructor-Led Coding Sessions Students found the interactive notebooks extremely helpful in understanding both the concepts and how to code, even (especially) when they did not have a Python programming background. However, due to the large diversity in student backgrounds, we recommend a crash course in Python (or a different programming language of choice) preceding the main course to allow everyone to start the course at roughly the same point. There will always be individual differences in students' aptitude to programming and problem solving, but this should not be conflated with an energy data science course, especially if there is only one such course being offered to students. We also found the online cloud platform to considerably help students unfamiliar with programming environments.
Collaborate, Collaborate, Collaborate Data science is a fast-paced discipline, and energy data science is no exception. Developments in the past few years have radically altered the face of the field, with advancements in transfer and domain-informed learning techniques providing greater impetus to the wide-spread adoption of data science techniques. The process of creating a curriculum and long-term vision for energy data science from scratch has benefited greatly from a collaboration across multiple MSc programs and universities. However, it remains unclear how this collaboration should be structured, and the role individual instructors should play in it. On the one hand, in many ways, working groups such as this one also need to go further than discussing curriculum towards jointly developing and maintaining course material, and ultimately test-beds and platforms.
While international organizations such as InnoEnergy certainly have a part to play in these developments, the onus is also on academics to take them further. As a result, to keep track of the ongoing developments in data science education at partnering universities, we have recently adopted a visual schema which requests course designers and instructors to mention the salient data science topics they intend to teach in a course along with the specific energy use cases and datasets they intend to tackle. This information is supplemented by target student audience and unstructured intended learning outcomes. As we mentioned earlier on, such an unstructured format provides us with a lot of information that is, on occasion, difficult to reconcile across universities and instructors. Bringing some structure comes at the cost of simplifying things, but allows quick aggregation of data to see (1) which data science techniques have not been covered, and (2) whether there are important energy use cases that still need to be discussed.
Cross-University Partnerships can Enable Education Testing and Research at Scale Our intent with course design is for all developed courses to be in the form of largely self-contained modules that can be taught in different programs with only minor modifications. To trial this, for the next iteration, we intend to run a cross-European summer course on energy data science in 2022. While the modalities for its organization are still under consideration, there is consensus that the summer course will be a flagship event open to all InnoEnergy MS students (and perhaps energy students at the partner universities). During the course, the students will work on concrete challenges from the industry in addition to receiving an education in data science. This course will serve as a precursor to develop this existing offering into a more holistic program which covers even more areas of energy data science, including the soft skills necessary to discuss analysis results with technical and non-technical stakeholders.
The coverage of material has also greatly expanded in this period, with the assigned credit roughly doubling (from 1. 5-2 ECTS in the first iteration) to 3 ECTS in the latest one. However, the approach has remained largely consistent: the course was structured to follow a flipped classroom approach with five lectures on the theory of data science. These lectures were followed by a practical lab session in which students worked with Jupyter Notebooks provided to them. The different course modules made use of a single dataset throughout, which consisted of electricity demand in a neighborhood of 200 different houses over an entire year, sampled at 15-minute intervals [9]. For the final session, students were also provided real-time electricity prices from Belgium. The individual sessions are organized as:.
We investigate the use of machine learning (ML) to forecast stock returns in the Brazilian market using a rich proprietary dataset. While ML portfolios can easily outperform the local market, the performance of long-short strategies using ML is hampered by the high volatility of the short portfolios. We show that an Equal Risk Contribution (ERC) approach significantly improves riskadjusted returns. We further develop an ERC approach that combines multiple long-short strategies obtained with ML models, equalizing risk contributions across ML models, which outperforms, on a risk-adjusted basis, all individual ML long-short strategies, as well as alternative combinations of ML strategies. Gu et al. (  2020 ), these methods are not well-suited to deal with the high dimensionality of modern financial applications, or potential nonlinearities.
The training set is used to fit the models. The validation set is used for hyperparameter tuning within a class of models (for example, to choose the optimal penalty parameter in LASSO). We apply a grid-search approach to select hyperparameters. Specifically, at each iteration of the expanding-window approach, we calculate the mean squared error (MSE) of forecasts in the validation set, and choose the value of the hyperparameter(s) with the lowest MSE. Finally, out-of-sample predictions are made for observations in the test set. 5  We use a pooled-data approach, stacking individual stock returns over all months of each training window into a single vector, and their predictors into a single matrix. 6 To represent this more formally, let y i, t+1 denote the return of stock i at month t + 1, n t be the number of eligible stocks in month t, and let τ k contain the indices corresponding to the months in the k-th training window.
Features. For each stock and each month, we have data on 62 features, which include fundamental indicators (i. e. derived from financial statement data) as well as technical indicators (i. e. constructed from market data). We also add 24 dummy variables to represent the firms' sectors, bringing the total number of variables to 86. The features are listed in Table  1. Due to the proprietary nature of the data, the names of some of the features are redacted (e. g. "Quality indicator 1"), and the exact definitions of all the features are not disclosed. We recognize that the lack of precise definitions of some of the features may raise questions regarding the replicability of the study. However, we believe that this is compensated by a few factors. First, we note that, even though we do not provide precise definitions of all the features, they are all constructed from public sources (i.
Concluding remarks In this paper, we explore the use of machine learning (ML) models to forecast stock returns in the Brazilian equity market, using a rich dataset of technical and fundamental indicators. While all ML models we test are able to produce portfolios that outperform the local market, the performance of traditional long-short strategies based on ML models are hampered by the high volatility of the short portfolios. We find that a simple Equal Risk Contribution (ERC) approach, which balances the risk contributions of the long and short components of individual ML long-short strategies, significantly improves their risk-adjusted returns. We further develop a multistrategy ERC approach that combines multiple long-short strategies obtained with various ML models, such that the risk contributions of all individual long-short strategies are equal, and the aggregate risk contribution of all long positions equals that of all short positions.
Participants Thirty-one young adults participated in this study, however two were rejected from analysis due to excessive EEG artifacts (>30% of trials rejected after ICA correction) and three for experimenter error/equipment malfunction. The behavioral and EEG data from the remaining (mean age 22. 7 years, SD = 2. 9, 14 male) were used for the behavioral and ERP analyses. All participants reported being right-handed, native readers of a Latin script (e. g. , English or Spanish), had normal or corrected-to-normal vision, and no history of neurological impairment. Participants were compensated $15 per hour for their participation, and informed consent was obtained from each.
EEG recording and processing EEGs were recorded from 29 passive tin electrodes (Electro-Cap) arranged in standard locations across the scalp (see Fig. 2 ). An electrode placed on the left mastoid served as a reference during recording. An electrode was placed under the left eye to help in identifying blinks. Electrodes were placed on the outer canthus of each eye to monitor horizontal eye movements. The impedances of scalp and mastoid electrodes were kept below 2. 5 kOhms, and all eye channels were kept below 5 kOhms. The EEGs were continuously sampled at 500 hz and amplified with a SynAmpsRT amplifier (Neuroscan) with a bandpass of DC to 00 Hz. Offline, ICAs were run on each participant's EEG and blink and horizontal eye movement artifact components were removed from the data  (Makeig et al.
Data analysis Behavioral data (proportion correct) were analyzed using a withinsubjects omnibus ANOVA contrasting the three kinds of Flanker stimuli (isolated vs. vertical flanked vs. horizontal flanked), the two Types of stimuli (letters vs. symbols) and the Visual Field of presentation (left vs. right). In the presence of interactions, separate follow-up ANOVAs were run comparing symbols and letters in just the isolated condition, and in just the flanked conditions. ERP data were measured as the mean amplitude in three different time windows consistent with prior studies using similar stimuli corresponding to the P1 (80-120 ms;  Emmorey et al. , 2017) , the N170 (140-200 ms;  Yum et al. , 2011)  and the N250 (200-300 ms;  Yum et al. , 2011). Analyses were limited to four temporo-parietal and occipital electrodes (T5/6 and O1/2) as is standard in N170 studies of letter processing (e.
Behavioral results We analyzed the accuracy data from the 2AFC task in within-subjects omnibus ANOVA contrasting the three kinds of Flanker stimuli (isolated vs. vertical vs. horizontal), the two Types of stimuli (letters vs. symbols), and the Visual Field of presentation (LVF vs. RVF). There was a main effect of stimulus Type (F(1,25) = 45. 34, p <. 0001) indicating that letters were responded to more accurately than symbols (0. 89 vs 0. 80), while a main effect of kind of Flanker (F(2,50) = 102. 32, p <. 0001) indicated that isolated non-flanked stimuli produced more accurate responding than both kinds of flanked stimuli (isolated = 0. 95 vs. vertical = 0. 80 vs. horizontal = 0. 77). Finally, the main effect of visual Field (F(1,25) = 25. 98, p <. 0001) revealed that RVF stimuli were responded to more accurately than LVF stimuli (0.
The role of preprints in the scientific production and their part in citations have been growing over the past 10 years. In this paper we study preprint citations in several different aspects: the progression of preprint citations over time, their relative frequencies in relation to the IMRaD structure of articles, their distributions over time, per preprint database and per PLOS journal. We have processed the PLOS corpus that covers 7 journals and a total of about 240,000 articles up to January 2021, and produced a dataset of 8460 preprint citation contexts that cite 12 different preprint databases. Our results show that preprint citations are found with the highest frequency in the Method section of articles, though small variations exist with respect to journals. The PLOS Computational Biology journal stands out as it contains more than three times more preprint citations than any other PLOS journal.
The relative parts of the different preprint databases are also examined. While ArXiv and bioRxiv are the most frequent citation sources, bioRxiv's disciplinary nature can be observed as it is the source of more than 70% of preprint citations in PLOS Biology, PLOS Genetics and PLOS Pathogens. We have also compared the lexical content of preprint citation contexts to the citation content to peer-reviewed publications. Finally, by performing a lexicometric analysis, we have shown that preprint citation contexts differ significantly from citation contexts of peer-reviewed publications. This confirms that authors make use of different lexical content when citing preprints compared to the rest of citations.
Introduction In recent years, the growing role of preprints in the publication process and the creation of a large number of publicly available preprint databases led to an increasing interest in the research community in scientometrics [see  Berg (2017 ), da Silva (2017a , 2017b , 2017c) ,  Kaiser (2017) ]. Indeed, a growing number of online repositories provide access to new research that is still in production and not yet validated by peers. This contributes to the acceleration of the dissemination of scientific results  (Abdill and Blekhman 2019; Larivière et al. 2014). The arrival of bioRxiv following the ArXiv model and its operation [see the work of  Pinfield (2001)  on the use of this type of service by physicists], is presented by  Berg et al. (2016). Also,  Fu and Hughey (2019)  show that having published a paper in bioArxiv is correlated with a high altmetric value.
The debate around preprints is important due to their complex nature. Preprint databases such as bioRxiv were initially intended to allow authors to receive feedback from peers and thus to improve their papers before their submission to peer reviewed journals  (Anderson, 2020). However, citing unverified work or nonvalidated facts may prove problematic as highlighted by the work of da Silva  (2018). Finally, a significant proportion of the preprints are never published in peer-reviewed journals  (Abdill & Blekhman, 2019). In times of crisis when research results need to be disseminated rapidly, preprints play an important role  (Añazco et al. , 2021). As health crises mobilize research efforts, the role of preprints as an underutilized mechanism for accelerating the dissemination of scientific findings is discussed by  Johansson et al.
(2018)  and  Majumder and Mandl (2020). More broadly, this phenomenon has invited community researchers to produce datasets to include the production of arXiv in a recommendation system  (Saier & Färber, 2019). According to the work of  Fraser et al. (2021) , COVID-19 preprints are consulted and distributed at least 15 times more than preprints that do not deal with COVID-19. This redefines the role and place of preprints in the world of scientific publishing and invites a reflection on the practices. Preprints have an informative role, and in times of crisis, practices can change, as shown by  Bordignon et al. (2021)  who propose a comparative study around the response to the COVID-19 pandemic. Their study shows that the percentage of structured abstracts has decreased in favour of non-structured abstracts during the pandemic for some preprint servers.
The question that arises today is the place of preprints alongside scientific articles and their citations: how do preprints contribute to the construction of new scientific knowledge while their validity, as sources, is not established by peer review? Will the use of preprint citations reinforce the critical attitude expressed in articles or will it weaken the edifice of knowledge? To answer these questions, one of the necessary steps is to study the nature and place of preprints in scientific journals. Preprints are frequently present in peer-reviewed publications as cited references. Direct citations to preprint databases such as arXiv, RePEc, SSRN and PMC all increased steadily from 2000 to 2013 according to the Scopus study by  Li et al. (2015). Also,  Fraser et al. (2020)  show that papers submitted to bioRxiv receive more attention from the scientific community than other publications.
This implies that in the publication process, the citation of works from preprint databases is becoming now a part of the publication cycle  (Hoy, 2020; Penfold & Polka, 2020). For this reason,  Desjardins-Proulx et al. (2013)  question the relationship between the preprint databases and the publishers with regard to the quality, but also the relevance of this type of production. Other ethical considerations arise such as plagiarism  (Giles, 2003). While citations to preprints play an essential role in research, the links between preprints and existing peer-reviewed publications are lacking. Recently, the work of  Cabanac et al. (2021)  contributes to establishing preprint-publication links. Such studies can provide information to allow a better interpretation of the citation contexts of preprints.
In this paper, we propose to study the citations of preprints, and to compare them to citations of peer-reviewed publications. More precisely, we focus on the frequency and relative part of preprint citations in peer-reviewed articles, as well as their evolution over time. Preprints contain recent methods and results that have not yet been peer-reviewed. For this reason, the knowledge on how such methods and results are actually cited and used in peer-reviewed journals will provide an important component of understanding the construction of new knowledge. We use as dataset the 7 PLOS journals, that are mainly in the biomedical field. We analyse both types of citations according to their position in the different journals and in the Introduction, Method, Results and Discussion (IMRaD) structure of articles.
The rest of the article is organised as follows: the next section presents the methods that were used to construct the dataset of preprint citation contexts and to perform the lexicometric analysis of the data. "Results" section presents the main results of this study. We report on the progression of citations over time, their frequency in the four sections of IMRaD and relative part related to all citations. Then we study the contributions of the different preprint databases in the PLOS journals. The final two subsections, "Verbs in preprint citation contexts" and "Correspondence analysis of citation contexts", are dedicated to the qualitative study of preprint citation contexts: the verbs that appear in preprint citation contexts, and a lexicometric analysis of preprint citation contexts compared to citations to peer-reviewed publications.
Creation of the dataset of preprint citation contexts We have processed all research articles published by PLOS up to January 2021. This corpus contains seven different journals, and the majority of the articles is in the biomedical domain. The largest journal, PLOS ONE, is multidisciplinary and contains also articles in various other domains. The PLOS corpus is available in XML format following the JATS (Journal Article Tag Set) XML schema, that is specifically designed for the representation and processing of scientific papers. Table  1  presents the number of articles and sentences per journal and the total size of the PLOS corpus. The editorial requirements of PLOS journals impose using the IMRaD structure for research articles. As a consequence, the vast majority of the articles follow this pattern.
Section classification We classify the sections into the four main section types ("Introduction", "Method", "Results" and "Discussion"). About 57% of the sections in the dataset are labelled as "intro", "method", "results" etc. in the "sec-type" attribute of the XML section element. We used these labels where possible. The remaining sections were classified using their section titles. To do this, sets of regular expressions were manually designed to account for the possible variations in the titles. For example, the Discussion section was found under 488 different titles such as 'Discussions', 'Discussion on practical implementation of proposed handover strategy', 'Summary and Discussion', 'Discussion and implications', 'General Discussion', 'Strengths and Limitations of the Study', 'Discussion/Conclusion', etc.
Processing and identification of preprint citations Article content was segmented into paragraphs and sentences. Citations were identified, their metadata were extracted and linked to bibliography items using the existing annotations in the XML source files (i. e. xref elements that point to bibliography items). The presence of citation ranges in the texts (e. g. [7]-[11]) requires supplementary processing. In fact, each such range in represented in the XML structure of the document by only two xref elements that are linked to bibliography items. Citations with numbers inside the range (e. g. [8], [9] and [10] in the range [7]-[11]) do not have xref elements in the XML. After the creation of these new links, we found out that they account for about 9. 5% of all citations in the dataset. Table  2  presents the number of bibliography items and citations per journal in our dataset.
To identify preprint citations, first we compiled a list of the names of known preprint databases. In order to account for all preprint databases, we used the lists of repositories from several sources 1. Preprint citations were then identified by processing their bibliography items' metadata and matching them against regular expressions designed to cover the list of preprint databases. Preprint databases can be of various types: multidisciplinary, thematic, linked to a publisher, etc. We account for this diversity in Table  3  that lists the preprint databases and the number of citations found in the dataset for each database. From the entire list of preprint databases, we identified 12 different sources that are cited in our dataset.