dataesr/scientific-paragraphs-categorization huggingface dataset:
After six months had elapsed, expert recommendations on measures to be implemented for the future had become more consistent and linked to evidence, signaling a crystalizing interpretation of the causes and effects of the disaster by the authorities involved. Nonetheless, as planned, we remained alert and actively collected data on the emergence of additional details up until one year after the event. For validation purposes we interviewed two expert informants, both of whom were GNR personnel who acted as liaison officers between the command center and the GNR operatives on the ground. The GNR was the entity responsible for closing roads, rendering assistance, evacuating people from endangered places, and creating safe passage for firefighters and others. The two interviewees covered the entire time span of the incident, from 17:00 on June 17 th to late afternoon on June 18 th.
Data analysis The analysis proceeded in three main steps: first, the construction of a detailed chronology of events (Appendix 1) to serve as a basis. Second, we analyzed the reports as our main source of data, categorizing the features that led to the improvisation. We treated documentation as text, analyzing it to identify relevant keywords that were subsequently clustered in conceptual categories around three types of codes: descriptive, analytical, and pattern  (Miles and Huberman, 1994). Descriptive codes included the conceptual categories observed in the field at the beginning of the study. These included technological breakdown, coordination failures, and pressure for action. For each code we defined keywords and developed a list of semantic descriptors of these keywords. In this way we were able to identify a particular category in the text.
Participants and procedure Gambling in the Digital Age Survey was targeted to Finnish speakers in mainland Finland in April 2021. The survey focused on gambling and addictive behavior. Participants (N ¼ 1530) were 18e75 years old (M ¼ 46. 67; SD ¼ 16. 42), and 50. 33% of them were male (n ¼ 770), 49. 41% were female (n ¼ 756), and 0. 26% reported other gender (n ¼ 4). The participants were from all major areas of Finland: 35. 29% were from Helsinki-Uusimaa region, 21. 50% from Southern Finland, 24. 84% from Western Finland, and 18. 37% from Northern and Eastern Finland. Data collection was administrated by Norstat, and all respondents answered the survey online. Participants were drawn from Norstat's Web-based panel. The response rate for the survey was 34. 60%, and the median response time for the full survey was 18 min.
We also used dummy variables for income (more than 3000V/month) and having children. Immigrant background was assessed with the question: "Was your mother or father born abroad?" We also asked respondents whether they have taken any instant loans (i. e. , pay-day loans). Instant loans were included in the data because they are considered major economic stressors that can lead to long-term financial difficulties. 41, 42 ocial media identity bubbles were measured with the 9-item Identity Bubble Reinforcement Scale. 43 This measure involves statements on social identification, homophily with others online, and reliance on information coming from others on social media. This type of bubble behavior is an important form of herd behavior, and bubble behavior has been recognized in cryptomarket trading as well.
Statistical analyses Statistical analyses were carried out using Stata, version 16, software. We report descriptive findings on different types of investing and other behaviors during the COVID-19 pandemic. Statistical modelling focused first on the analysis of background factors associated with different types of investing. This was conducted with multinomial logistic regression using non-investors as a reference group. Table  2  reports relative risk ratios (RRRs), standard errors (SE), and the statistical significance of results (p). RRRs are interpreted as odds ratios (ORs) in binary logistic regression (RRRs >1 indicate higher risk, and RRRs <1 indicate lower risk). Associations of different types of investing and excessive behavior and mental health problems are analyzed using negative binomial regression owing to the overdispersion of scales measuring excessive behavior.
AI is supported by access to databases, big data, cloud computing facilities that require a new stakeholder network  (Elia et al. , 2020). The relationality also enables substitution mechanisms inducing to replace one human resource with digital entities. These entities-software systems, web applications, and algorithms-are able to process data in real-time, support effective matching among involved actors, provide recommendations and comments, and interact with humans to execute routine and complex tasks useful to support the entrepreneurial processes of the ecosystem  (Elia et al. , 2020). Combination and/or generation mechanisms during venture creation are contingent on both the technologies' specificity and their relationality (von  Briel et al. , 2018). Specificity and relationality should be combined together to activate the above-mentioned mechanisms.
The database collects over 500 startups from France, Germany and Sweden, considered as top startups from a group of associations and members of the German Entrepreneurship, the German Accelerator program, Vinnova, Sweden's Innovation Agency-appliedAI (Germany), Ignite Sweden, AI Sweden, RISE Research Institutes of Sweden (Sweden) as well as Hub France IA (France). According to Shivon Zilis' landscape of machine intelligence, startups are sorted via four categories which cluster firms on the pointof-view of companies that want to use AI in their businesses: Industry, Technology Type, Enterprise Intelligence and Enterprise Function. To define our analysis sample, we select the list sorted for technology type to have additional information of which type of technology intended as platforms, frameworks, infrastructure or application is useful to support entrepreneurs in business creation and competitive advantage.
The prospecting phase Findings support our propositions on the role of AI in the prospecting phase of venture creation. Two cases are representative of that phase: MyDataModels and Hasty. ai. MyDataModels develops and markets TADA, an augmented analytics platform that helps professionals understand and treat data. The platform is aimed at researchers, tech experts and business experts. Our investigations look at functions for researchers that refer to knowledge-intensive startups or projects. TADA, in fact, supports researchers during information and data searching. The solution is characterized by a genetic algorithm suited for numerical datasets with a very small number of samples. This characteristic is relevant for new ventures that do not possess a large amount of data or for certain industries that do not have large data for implementing their idea.
In addition, TADA is characterized by a high level of usability; TADA does not require coding or machine learning skills. "The advantage of TADA is that it does not require AI knowledge, but only the knowledge of its job" Alain Blancquart (co-founder). Therefore TADA extracts key values from small data samples, exploring data in minutes (no hours or days). In other terms, AI acts with a compression mechanism during the prospecting phases. Furthermore, it leads new firms to consume less human resources and financial resources for collecting data. Similar mechanisms are assessed in Hasty. ai. Hasty. ai supports vision AI practitioners, AI startups, developers developing best-in-class annotation tools. Hasty. ai proposes AI models that create multiple types of annotations (classification, object detection, semantic segmentation and instance segmentation).
Annotations are used for training models in digital ventures or research. Hasty. ai enables customized annotation according to the user's needs, who "adopt an agile approach rather than a waterfall approach to build their models" The speed and ease of use have allowed us to accelerate our mission to improve global health in the world's most underserved communities". Therefore Hasty. ai enables compression mechanisms offering quicker and more effective annotation. Furthermore, it triggers a conservation mechanism eliminating the cost of annotators service providers saving a considerable annotation budget and enabling firms to do more work in-house, automating 90% of the work after 1,000 images. It enables a substitution mechanism since the need for human supervision decreases: with enough data, firms batch process the rest, or a specific part, of your dataset in one click; while with the detective functions, Hasty App reduces the need for manual quality assurance by 95%.
Designing AI implications to create new artifacts. TADA allows the resource replacement with the genetic algorithms; it avoids hiring a data scientist or establishing a partnership to receive data triggering substitution mechanisms. Differently, the Peltarion platform supports firms in assessing other propositions. The platform empowers anyone to design and deploy AI without a single line of code. It offers an all-in-one space for efficiently testing, comparing and realizing AI ideas. The platform's nocode environment lowers time-to-value and simplifies the AI onboarding process through intuitive guides and a graphical interface. Peltarion helps define the AI opportunities in organizations, create proof of concepts, work with your data, or build the products and services around your operationalized AI models.
For instance, it contributed to developing the detective pen supporting Scibase, a Swedish startup producer of medical devices. The pen recognizes atopic dermatitis using an AI model produced through Peltarion. Scibase's CEO Simon Grant declares, "The neural net uses the data to help us find the useful answers that we are looking for. The Peltarion Platform allows us to focus on solving problems. Faster. The platform takes care of a lot of the mechanics of AI. It takes away a lot of the tricky stuff and automatically handles it. It enables us to try different datasets and experiment faster". Again the platforms support the creation and the empowerment of App-based on Ai models without coding. Concerning mechanisms, the Peltarion platform enables the creation of new apps or devices (creation mechanisms).
, 2008; Escrig-Olmedo et al. (2017) ;  Testa, Mirohnychenko, et al. , 2018) , environmental performance indicators should be aggregated into indices or composite indicators that encompass both process and outcome perspectives. Accordingly, we have built an aggregated environmental performance index by adding the different environmental practices related both with processes and outcomes available in the ESG Thomson Reuters database. Thus, more environmental practices carried out by the firm, the higher will be its environmental performance. Therefore, the following processes or outcomes indicators signal if companies have carried out each of the initiatives or not (yes/no). Regarding outcomes (those indicators that materialize in an effective reduction of environmental impact) we have taken into consideration the next initiatives: (i) biodiversity impact reduction; In order to build the four environmental positioning clusters, we decided to split both axes-environmental performance and environmental disclosure-into low and high positions.
Our work overcomes the very scarce previous empirical fragmented contributions based on limited samples, experiments, and managerial perceptual and subjective information by developing an integrated theoretical framework and an empirical study in a wide longitudinal sample 2016-2019 of international publicly industrial firms from the ESG scores database. The results indicate that companies falling into different environmental positioning have different market firm performances. Thus, our empirical evidence provides very interesting and novel insights showing that markets seem to reward companies that have a high level of environmental communication irrespective of their environmental performance. Furthermore, the international and multiindustry character of our sample enriches and expands previous fragmented and scarce empirical existing evidence.
Population, sample, and data sourcing The research setting for this study is a panel data set of European, American, and Canadian public industrial firms in 2016, 2017, 2018, and 2019. Because of the small number of firms included and the missing data existing in early years 2008-2015, we decided to carry out our empirical research in the period 2016-2019. As the number of companies differs from year to year, being of 505 firms the largest sample size in 2019, the database is of a semi-panel format. Data were drawn from the ESG database of the Thomson Reuters Eikon, the world's largest environmental, social, and governance rating database available. ESG database comprises relevant, auditable, and systematic quantitative and qualitative company-level data on public companies worldwide for several years, which makes it an adequate data resource for carrying out longitudinal studies.
ii) staff transportation impact reduction; (iii) NOx and Sox emissions reduction; (iv) e-waste reduction; and (v) particulate matter emissions reduction. In turn, regarding processes (those indicators whose materialization into a reduction of the environmental impact cannot yet be specified) we have analyzed the next initiatives: (i) emissions trading and (ii) environmental restoration initiatives. In a similar way, and as Ortas, Gallego-Álvarez et al. (2015) proposed, we have constructed an aggregated environmental disclosure index by adding the 12 indicators available in the same database. Concretely, the indicators show if companies do report on the following issues (yes/no): (i) CO2 equivalent emissions total; (ii) total waste to revenue; (iii) waste recycled to total waste; (iii) self-reported environmental fines; (iv) environmental R&D expenditures to revenues; (v) ozone-depleting substances; (vi) EMS certified; (vii) total hazardous waste to revenue; (viii) water pollutant emissions to revenue; (ix) environmental controversies count; (x) recent environmental controversies; (xi) GRI report guidelines; (xii) CSR sustainability external audit.
ACKNOWLEDGMENTS The authors would like to thank for the financial support to the  Spanish Ministry of Economy and Competitiveness  (Project  ECO2012-38190 , Project  ECO2015-65251-P ),  Spanish Ministry of Science and Innovation  (Project  PID2020-117564GA-I00 ),  Programa Movilidad Investigadores Senior en el Extranjero and Banco Santander-UCM  (Project  PR26/16-15B-1  and Project  PR108/20-01 ).
( ii) staff transportation impact reduction; (iii) NOx and Sox emissions reduction; (iv) e-waste reduction; and (v) particulate matter emissions reduction. In turn, regarding processes (those indicators whose materialization into a reduction of the environmental impact cannot yet be specified) we have analyzed the next initiatives: (i) emissions trading and (ii) environmental restoration initiatives. In a similar way, and as Ortas, Gallego-Álvarez et al. (2015) proposed, we have constructed an aggregated environmental disclosure index by adding the 12 indicators available in the same database. Concretely, the indicators show if companies do report on the following issues (yes/no): (i) CO2 equivalent emissions total; (ii) total waste to revenue; (iii) waste recycled to total waste; (iii) self-reported environmental fines; (iv) environmental R&D expenditures to revenues; (v) ozone-depleting substances; (vi) EMS certified; (vii) total hazardous waste to revenue; (viii) water pollutant emissions to revenue; (ix) environmental controversies count; (x) recent environmental controversies; (xi) GRI report guidelines; (xii) CSR sustainability external audit.
Observations were an additional source of data. As all interviews were conducted on the company premises, we were able to observe communications between the employees, and we were offered guided factory visits with explanations about different machines, manufactured parts, and production organization. We also collected secondary data in the form of documents provided by the SMEs and the local industry support organizations, such as public relations material, brochures or newspaper articles, and company website content. This data provided general information about the companies and, to some extent, about their internationalization pathways. We developed a coding structure for coding the interviews. We used the codes to condense the data as a first step. We asked a researcher who had not taken part in the data collection to code a set of eight interviews, one from each case.
Reaching this goal requires a diachronic and comparative analysis of market organizations and large, high-frequency datasets. However, history can only be written from sources. In-depth markets' descriptions can mainly be delivered for a limited number of well-organized and relatively 'successful' exchanges compared to all the trading venues that have existed over time. Sources are often rare for many less formal markets, particularly if they ceased to exist, in spite of the fact that these markets at some point played an important and sometimes central role. Studies on market organizations are needed to understand not only price formation and dissemination, but also the large set of data produced by historical markets. Large and high-frequency market data are required for both event studies and studies of long-term dynamics.
The creation of these datasets requires huge efforts to conceive harmonized data semantics and documentation practices, as well as large investments in technologies capable of transforming old paper records into new digitized versions at large scale. The work with historical sources, the transcription of data in electronic format, their harmonization within common semantics and their transformation into coherent time series require a large range of interdisciplinary competences and local knowledge to deal with the different aspects of markets embedded in very different historical contexts. The goal of the EurHisFirm project is to assemble and develop these competences and knowledge. Submitted:  April  Accepted:  June  First published online:  January .
To address this question, our research bases on 10 case studies in five industries located in six European countries. A total of 19 semi-structured interviews as well as publicly available and internal data were collected from the relevant years before and during the pandemic. These data are analyzed in three steps. First, we conducted an inductive analysis of the actions taken during the pandemic and the SC scale at which they are exercised. Second, a deductive content analysis is applied to understand which SCRes approaches and capabilities these actions represent what learning can be observed. Finally, we inductively identified the cases' SC sustainability trajectories. The findings contribute to the literature as follows: (1) We respond to the broader call of theory-building around COVID-19  (Craighead et al.
Data collection In total, 19 knowledgeable medium-to top-level firm representatives were interviewed between July 2020 and February 2021. As the research setup is cross-functional, we approached informants from the operations, SC, and sustainability function of the firm. Whenever possible, we asked for more than one respondent to avoid a single informant bias. All interviews were conducted in English and German and lasted on average 54 minutes. The research scope is limited to European interviewees and their perspectives with some minor reflections on African, South American, and Asian suppliers. Table  1  illustrates detailed information for each case. -  ---------------------------INSERT TABLE 1 APPROXIMATELY HERE ------------------------- To develop the interview guideline, a research protocol was applied to ensure the reliability of the data  (Yin, 2018).
The interview guideline (see supplementary file A) aimed to reflect on what SCRes related actions were conducted in the cases as well as the interviewees' interpretation of how and why the actions were adopted. To this end, the guideline centered on SC sustainability actions before and during the pandemic, SCM related learning during the pandemic, and the envisioned path of the case firm as well as industry after the pandemic. This focus during the interviews on actions, learnings, and future plans allowed for a clear deductive identification of the specific SCRes approach (engineering, ecological, and social-ecological) during the following data analysis, as described below. All interviews were conducted remotely via Skype, Zoom, or MS Teams, and recorded given the participants' consent. Interview notes were taken during these interviews and secondary materials (e.
Thereafter, a grouping of the actions into the SC scales was realized through axial coding  (Strauss and Corbin, 1990) , which helped to inductively identify the SC sustainability trajectories. All coding was conducted manually and supported by the MAXQDA software that facilitates the definition and documentation of codes, categories, and coded text passages. The deductive approach was applied to characterize the previously defined SC sustainability actions according to pre-defined categories: regarding the sustainability dimensions addressed and the adopted SCRes approaches  (Adobor and McMullen, 2018; Wieland and Durach, 2021) , the capabilities leveraged through the actions  (Pettit et al. , 2010) , and the sustainability dimensions addressed. The SCRes approaches are based on the extensive characterization of the three approaches by  Adobor and McMullen (2018)  which were later enriched by  Wieland and Durach (2021).
Moreover, the interviews and most recent secondary data underline that the actions taken during the pandemic will persist in the long-term. Finally, two cases illustrate medium importance to one of the three TBL dimensions. Case 6 shows strong environmental and economic sustainability, but medium social sustainability. Case 10 shows strong economic and social sustainability, but medium environmental importance. In the latter two cases, the pandemic actions are mentioned to be exercised during the time limited to the pandemic. Through the comprehension of SC sustainability experience and previous engagement, we can better identify how firms learn SCRes in turbulent environments. Therefore, we analyze the interviews and secondary data to identify if the pre-pandemic sustainability strategy was exercised for one, two, three, or more years.
Data selection As a first step, for each country local experts in the field of primary care specific CME training for GPs we contacted (MD for the UK, HM for Norway, GJD for the Netherlands, ADS and BDV for Belgium, AA, SB and GE for Germany, TR for Switzerland and BD, FR, PB, and PTL for France). These experts were characterised by the fact that they had acquired specific knowledge about the respective country-specific GP training system over many years as academic general practitioners or as experts of corresponding specialist institutions. For each country, experts performed literature searches for regulations and formats for general practice continuing education. In addition to human medicine literature databases such as Pubmed, grey literature (writings of countryspecific institutions and associations, congress reports, academic and non-academic writings, etc.
Data collection instruments The semi-structured individual interviews were conducted using an interview guide based on dimensions and constructs of the CFIR. Interviews were conducted face-to-face or by phone and lasted 1 hour on average. To make the interviews more dynamic, the interviewees were emailed a card with information about CECC, as well as a link to the CECC website ahead of time so that they could access additional information (https://cancer-code-europe. iarc. fr/index. php/es/).