dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, this dataset was subdivided into several files each of which has a window of 10 Mbp with an overlap of 1 Mbp between each two successive windows. Subsequently, we launched FLK/hapFLK on all files one by one with a specified number of clusters set to K 25. The analysis was launched with the option of keeping the Outgroup when computing FLK/hapFLK scores. We applied the approach of  Storey and Tibshirani (2003) , implemented in the q-value R package  (Storey et al. , 2015)  to control the false discovery rate (FDR) based on FLK/hapFLK p-values. Lists of genes that include or less than 5 kb away from the identified candidate SNPs (Downstream 5′-end and upstream 3′end) were established and used for the Gene Ontology (GO) enrichment analyses. Similarly, outlier genomic regions were constituted of 50 kb segments surrounding outlier SNPs.
This selective sweep was associated with the genes RXFP2 and ENSOARG00000011616. The other strongest selective sweeps were related to 1) the region located on Chr14: 13,329,709-14,250,423 differentiating Sardi and associated to 8 genes; 2) the region located on Chr19: 2,143,797-2,261,064 differentiating Sardi and Beni Guil from the other breeds and associated with intergenic SNPs; 3) the region located on Chr16: 34,551,942-34,646,856 differentiating Timahdite and associated with intergenic SNPs; 4) the region located on Chr2: 84,619,111-84,759,276 differentiating Timahdite and associated to the BNC2 gene and some intergenic SNPs (Figure  2A ).
Both research projects found this "self-signaling" approach to be promising. The application of user generated data analysis techniques from social networks has been explored in manifold ways. Pajo et al. (2015)  propose a data mining technique for identifying lead users. In initial exploratory work, they focused on a survey population of users who discuss or follow camera lens product related topics on Twitter. To train the search algortihm, they used a sample of validated lead user questionnaires containing clusters of lead and non-lead Twitter users interested in that single product. They suggested that data mining techniques such as the one they explored can minimize the resource and time costs in identifying lead users, and that future work should develop the idea further. Twitter data was also examined by  Tucker and Tuarob (2015)  to analyze user generated content from potential lead users who shared content around 27 smartphone models.
Material eliminated includes producer innovations, duplicates, and very minor improvements and alterations to existing kiteboarding hardware. Step 4 User Innovation Trend Analyses 5 fundamentally radical LU innovations ranked by trend analyses to assess commercial promise Test of popularity / commercial value of identified consumer user innovations via trend analyses of frequency of relevant UGC contributions related to each identified user innovation, and frequency of searches for that innovation by Google search engine users. as a commercial product by a producer firm. We found that at least 12 and perhaps as many as 15 of these innovations were later commercialized by producers. This is in line with other research evidence that many user-generated innovations are of commercial value. The newest innovations were the least likely to have been commercialized at the time of data collection, suggesting that application of this semantic search method can indeed identify very recent user innovations that may represent as-yet unexploited commercial opportunities for producers.
Future research could investigate the practicability and efficiency gains of applying the latest advancements in the unsupervised machine learning domain for NLP such as Bidirectional Encoder Representations from Transformers (BERT) published by Google AI Language that enables a deeper sense of language context understanding compared to the word space models by using a bidirectional language representation approach  (Devlin et al. , 2018). It appears to us from our initial experimentation that only some of the search terms required in any particular search area will be unique to that specific area (e. g. , "chickenloop" in the case of kiteboarding). Other important search terms are likely to be general across cases, and these could be supplied to searchers as part of a semantic word search toolkit.
Step 1: Scraping the open web for relevant user-generated content The first step in our study process was to collect all of the relevant user generated data in the field of interest leveraging key terms and expressions within the kiteboarding sport domain. This data is the foundation for further analytic steps to identify need solution pairs within the relevant data corpus. The data collection included publicly available user generated content from blog posts, forum posts, consumer review, social media and microblogging platform posts (e. g. Facebook, Instagram & Twitter). The data crawling approach used was, at the time of the study, fully compliant with the data protection and personal data management regulations such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) and the Singapore Personal Data Protection Act 2012 (PDPA).
We collected publicly available information only, private status or any data that has been restricted by the user was not accessed and included in the analysis. The analysis was conducted on voluntarily posted content on the Internet with consent regarding the use by third parties. The process to gather this information must ensure that niche web sources are included, outside the social media mainstream, such as expert forums where potential lead users and experts gather to share their expertise, professionalism and passion. In our kiteboarding study, we found after collecting and analyzing the collected data, that more than 90% of the consumer generated content was generated in specialist forums and other niche sources such as kiteforum. com, seabreeze. com. au or powerkiteforum. com. Content from large social media or digital touchpoints with large traffic on a monthly basis such as YouTube, Reddit, Twitter or Facebook play an insignificant role as exchange source between experts.
It also contains a duplicate detection system to remove copied and duplicate posts from the dataset, ensuring content is only collected once. (Note, we collected only posts written in English to avoid translation difficulties in attempting to understand technical posts written in other languages. ) The process we describe starts with finding the most relevant set of sources where user generated content is present. This process step requires various iterations, comparing a "narrow search" first and compared it to a with a "broad search", to find the ideal search algorithm that retrieves the most relevant content universe for the main purpose of the studyidentifying functionally novel user innovations. The comparison between the two different approaches requires an expert validation of a random sample of n ~100 comments from both of the search methods with the aim to identify which methodologic approach leads to be most relevant data universe.
It might, for example, mainly consist of producer driven innovations. Second, as we found, the direct search for "innovation" or "invention" was too narrow and did not well reflect the natural language used when kitesurfers talk about their innovations. Our analysis showed after process iterations and optimizations in the analysis steps that the most effective semantic algorithm needed to include all possible natural speech variations to isolate the "Innovation Concept" based on words and expressions that reference to innovation such as created, design, DIY, homemade, developed. We also needed to come up with something combining those words and phrases with "first person speech" to ensure that the comment is about user innovation (e. g. "I developed") but not manufacturer innovation (e. g.
The corpus texts are encapsulated in a space of vectors. Within this dimensional space all vectors have certain orientations and relationship with each other. The closeness and distance of vectors within the dimensional space was calculated using cosine similarities. The cosine similarity (also referred to as cosine distance) is the angle between vectors. Two vectors with the same orientation have a cosine similarity of 1. Within GenSim or spaCy, analysts today have free access to convenient Python libraries with intuitive interfaces that are ideal for the described approach, and are widely adopted. With spaCy users can access pretrained models in various languages for a broad variety specific word embedding needs (e. g. "en_core_web_lg" released in November 2019). For our research application we used a pre-trained word space model with embeddings to assign word vectors, token vectors, part of speech tags, dependency parse and named entities.
With this approach we deconstructed sentences from the relevant user content universe using tokenization techniques to identify the posts that clearly indicated the user innovations. spaCy for Python offer a collection of natural language processing tools that are ideal to create the syntactic sentence segmentation for the described purpose. The combination of both analytic approaches described above enabled us to reduce the already reduced kiteboarding innovation content corpus from 6065 posts down to 453 likely user innovation posts. After applying the second semantic filter algorithms we found that 7. 5% of posts within the innovation content corpus also matched both the first-person speech criteria. The remaining content corpus of 453 now represents the semantic innovation concept used in a first-person speech.
These, as it turned out, were innovations with the potential to disrupt the established sport or introduce new directions in the domain. We developed specific search queries to scrape user generated data around the selected innovations considering entire language spectrum around the innovation from the time period of July 2015 until March 2018. E. g. The search query for "Electric Hydrofoiling" also entailed the term "eFoil" which references to a development community around electric hydrofoiling. Similar to the process iterations described in process step 1, we compared the results from narrow and broad scraping approaches to obtain the most relevant, all-encompassing and effective search approach. After determining that the 5 search queries were satisfactory from a content relevance standpoint, we extracted the absolute mention volume on a quarterly basis.
We collected the consumer search query data around the five innovations using Google Search Trends and extracted the relative search volume to compare it against the social mention volume. Google trends provides only relative search volumes of the keywords of interest with the maximum search index value of 100 for the specified period. For multiple searches that may require capturing all relevant content around a specific innovation, the highest volume in for a given time period is indicated as 100 and the months and searches are sized proportionally to this highest value to establish even growth benchmarks. After the search data for all five fundamental innovations were extracted and aggregated into quarters, we visualized the search and social data trends over time for the 5 innovations.
Suggested tools for semantic analysis Tools evolve quickly, but at the time of writing, we suggest the following free semantic analysis packages that are openly available to all. • OpenNLP for R (A machine-learning based natural language processing toolkit supporting a broad variety of common analytic needs such as parsing, tokenization or sentence annotations. • Ggraph for R (A popular and user-friendly R package for advanced semantic analysis using co-occurrence statistics. This useful toolkit reveals how expressions and topics in the specific domain of interest are semantically related in the authentic usage context. ) • LDAvis for R (A topic modeling package to explore the user generated data corpus interactively by the most salient expressions clustered into topic concepts by meaning.
Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Longstanding problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems.
Galaxy Clustering The distribution of galaxies in space proves to be surprising complex from the viewpoint of spatial point processes. Figure  1  shows the distribution of galaxies in a slice of the sky out to redshift 0. 14, equivalent to distances ∼600 megaparsecs (Mpc, a parsec is about 3. 26 lightyears). The dataset pictured here is part of the Sloan Digital Sky Survey (SDSS), one of the most successful astronomical projects of modern times that produced thousands of important studies. The galaxy distribution is very nonstationary and anisotropic on smaller scales (< 200 Mpc) but mostly stationary on large scales. The pattern, commonly called the 'large-scale structure' of the Universe, roughly resembles a collection of contiguous soap bubbles where galaxies are distributed along curves 'filaments' and sheets surrounding 'voids'  (Zeldovich et al.
2010) , hyper-optimized gradient boosting regression, Gaussian processes regression, and hybrid schemes. Galaxy morphology from images can supplement photometric measurements when neural networks are used. More recently, some more advanced methods have been used, including the use of different flavors of deep convolutional networks to derive photometric redshift directly from multi-band images  (D'Isanto & Polsterer 2018 , Pasquet et al. 2019) , and to build a morphology-aware photo-z estimator  (Menou 2019). Comparisons of performance are presented by  Dahlen et al. (2013) ,  Rau et al. (2015) ,  Salvato et al. (2019), and Schmidt et al. (2020). However most methods fail to achieve better than ∼ 2% accuracy. The relationship between photometry and spectroscopic redshifts are not only nonlinear, but degeneracies, heteroscedasticity, and 'catastrophic outliers' abound in the datasets.
A further caveat encountered by machine learning based methods is a common mismatch between the pa-rameter distribution of the training (spectroscopic) and target (photometric) datasets as portrayed in Figure  3. This is connected with the nature of spectroscopic measurements which, within the same survey, demands higher quality data and consequently delivers lower redshift and higher metallicities galaxies then the photometric counterpart. In cases where spectroscopic and photometric samples have similar coverage in magnitude/color space, it is feasible to adapt the spectroscopic sample using Domain Adaptation  (Beck et al. 2017). However, in most real-data scenarios this assumption will not hold. An alternative approach relies on active learning to improve the training set by making sensible decisions to query extra galaxies from which one could measure the spectra  (Vilalta et al.
Modeling Multiplanet Systems One of the great excitements of modern astronomy is the clear evidence that most stars in the sky have their own planetary systems. The original discovery was based on radial velocity measurement of the host star by  Mayor & Queloz (1995)  who were consequently awarded the 2019 Nobel Prize in Physics. The star is pulled back and forth as it is orbited by planets which are usually too close to the star to be resolved in a telescope image. Spectra to obtain the star's radial velocity are repeatedly obtained to detect the resulting periodic Doppler redshift and blueshift. Extremely precise spectrographs are needed, as the velocity shifts may be less than 1 meter/sec, a tiny fraction of the velocity of light at 300 million meters/sec. The resulting dataset is a time series of radial velocity measurements.
Scatter within a bin is often non-Gaussian with outliers from poorly quantified measurements. The choice of k is arbitrary, the probabilities for False Alarms in the multiple trials has not been addressed, and the periodogram is subject to strong aliasing effects when true periods are present. Perhaps the most insidious problem is that many stars show autocorrelated but aperiodic variations that interact with the irregular cadence to produce false peaks in the periodogram for short duration datasets. None of these statistical issues have been examined by mathematical statisticians despite its use in ∼ 1500 astronomical papers over four decades. Variants of epoch-folding periodograms have been developed. Dworetsky (1983)  proposes an unbinned L1 version of the PDM nicknamed the 'minimum string length' periodogram that alleviates some problems, but in practice seems less sensitive than PDM.
So far, no astrophysical signal has been reported  (NANOGrav Collaboration et al. 2018). The NAGOGrav time series data present considerable scientific, statistical and computational challenges. This analysis starts with multicomponent, nonlinear model of a non-Gaussian and non-stationary time series  (NANOGrav Collaboration et al. 2015 , 2018). A model of pulse arrival times must take into account: the astronomical characteristics of the pulsar (spin period, proper motion); gravitational effects from any stellar companions; time-varying dispersion measure variations from the propagation of the pulse through the inhomogeneous Galactic interstellar medium; autoregressive 'red' noise of unknown origin with timescales of weeks to years; white noise (radio receiver noise and some additional component of unknown origin).
The Statistical and Applied Mathematical Sciences Institute has run several astrostatistical programs. In some respects, statisticians can readily enjoy the fruits of astronomical observations for statistical study: a vast range of data are freely available. The astronomical community has a long-standing tradition, often legally binding, of making both raw and analyzed data accessible on the Web. The U. S. National Aeronautical and Space Administration and European Space Agency operate large science archive centers for satellite observatories. Ground-based data are available from the European Southern Observatory, U. S. National Optical Astronomy Observatory and National Radio Astronomy Observatory, and other major institutions. The International Virtual Observatory Alliance provides an integrated interface to these and hundreds of other distributed databases.
The Vizier and SIMBAD services from Frances Centre de Donnes Astronomique, and the NASA/IPAC Extragalactic Database, provide convenient Web-based access to published tabular data. The Smithsonian/NASA Astrophysics Data System provides superb bibliographic services with full-text, references and citations for nearly the entire astronomical research literature. But in other respects, conducting astrostatistical research requires considerable care. The datasets are often subject to selection biases inaccessible to non-experts. The scientific questions are often complex, so analyses must be designed within the context of established research programs. Statistical studies in cosmology can be particularly challenging. Analyses of cosmic microwave background maps need to be interpreted within the ΛCDM cosmological model, and three-dimensional cosmography of Dark Matter combines complexities of both statistical weak lensing signatures and the imprecision of photo-z distances.
In this paper we outline the results of a large scale survey of European academics about existing competencies in the teaching of computer ethics in Computer Science and related disciplines (Ethics4EU, 2021). The survey was completed by respondents from 61 universities across 23 European countries. Respondents were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught (for example, as a standalone course or embedded within other modules), the background of staff who teach computer ethics and the scope of computer ethics curricula. Data was also gathered on teaching and learning methods used (theory, case studies, practical work) and how computer ethics is assessed.
It is worth noting that much of the research on the teaching of computer ethics focuses on US institutions and there is a lack of comprehensive data on the teaching of computer ethics in Computer Science from a European perspective. Our study aims to contribute towards shedding more light on what is the situation regarding European Computer Science and related programmes. In the next sections, we outline the results of a survey of 61 universities across Europe designed to understand the extent to which and how computer ethics is taught on Computer Science and related programmes across Europe.
Methodology We designed an online survey to better understand existing practices in the teaching of computer ethics in Computer Science programmes at European Universities. The survey was created using Lime Survey software and was developed in the English language only. The questionnaire was sent to all members and networking partners of Informatics Europe and European Digital Learning Network who are partners in the Ethics4EU project with a reach of reaching 152 European Universities from 30 European countries. It was also publicly available from the Informatics Europe website and shared on a range of social media platforms, including: Twitter, Facebook and relevant LinkedIn groups. The questionnaire was deployed in January 2020 and was available online for 6 weeks. Over the six weeks, weekly reminders asking people to fill out the online questionnaire were sent.
Section B-Institutions that do not teach ethics as part of their Computer Science and/or Computer Science related programmes A total of 22 responses from 61 countries were received from academic institutions that do not teach computer ethics on their Computer Science (and related) programmes. Of those responses, 21 came from institutions that teach all academic subject areas and only 1 from an institution that focuses on technical subjects. In our dataset, almost one third (7 out of 22) of those responses were from Italian institutions. In response to this overrepresentation, Jackknife resampling was applied to estimate the bias of the sample and no significant impact was found. The rest of the institutions were spread geographically across Europe. How important do you think it is that ethics is taught on Computer Science and/ or Computer Science related programmes? In academic institutions that do not teach computer ethics, almost two-thirds (63%) of the respondents' consider the teaching of computer ethics as either being "Important" or "Very Important" for Computer Science (and related) programmes (Table  6 ).