dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement ERA5-land reanalysis fields with hourly temporal resolution can be downloaded from the Copernicus Climate Change Service (C3S) Climate Date Store (https://cds. climate. copernicus. eu/%23%21/search%3F-text%3DERA5%26type%3Ddataset). Leaf area index (LAI) climate date record (CDR) can be downloaded from NOAA National Center for Environmental Information (NCEI) (https://www. ncei. noaa. gov/products/ climate-data-records/leaf-area-index-and-fapar).
Materials and Methods In the development of the Family Needs Checklist (FNC), an approach for checklist development established by Winters et al. [64]  was utilized. It is a three-phase development process, including (1) a review of the existing literature, (2) understanding the needs and workplaces of the users, including a multidisciplinary group in the design, and (3) using an iterative approach for the rigorous pilot testing and validation of the checklist  [64]. In the present study, phase one and partially phase two, including a multidisciplinary group in the design, were completed. Phase one involved compiling and updating the evidence base for content and the creation of a preliminary checklist. A systematic review of databases was completed to retrieve review articles focusing on risk factors of CM in families with children and risk assessment tools.
The foundation hosts The Finnish Centre for Evidence Based Healthcare: A JBI Centre of Excellence  [72]. The following databases were searched: Cochrane, DARE, JBI, PROSPERO, PubMed/ Medline Ovid, CINAHL, Academic Search Ultimate, PsycINFO, SCOPUS, and MEDIC. The search included both MeSH search terms and keyword synonyms, using a search strategy developed by a librarian specialist in Medline and subsequently modified for use in the remaining databases. References in related review articles were also reviewed. Reviews were included in the literature review if they addressed either the risk of child maltreatment or the method used to assess it. Searches were confined to systematic reviews, scoping reviews, meta-analyses and -syntheses published in English, Finnish or Swedish between 1 January 2014 and 30 April 2021.
We excluded single studies and systematic reviews whose focus deviated from child maltreatment in the family or its risk conditions. The quality of evidence was examined by two reviewers using the JBI Critical Appraisal Tool for Use in JBI Systematic Reviews (https://jbi. global/sites/default/files/2019-05/JBI_ Critical_Appraisal-Checklist_for_Systematic_Reviews2017_0. pdf, accessed on 18 July 2022). The data were analyzed using the content analysis method. The PICO, search strategy and flowchart figure of the systematic review and meta-analysis selection process are available as (Supplementary Material Table  S1  and Figure  S1 : Research questions, PICO, search strategy and flowchart of the systematic review and meta-analysis selection process). The identification and conceptualization of the CM familial risk factors were based on the Finnish National Nursing Guideline (2015)  [52]  and reinforced with the research data from updated guideline  [6].
The checklist was reviewed by trainees and/or experts in the ERICA training cohorts 1 and 2 that were held between December 2020 and May 2021. The FNC was included in one of the training modules and introduced and discussed during the training session. The feedback was given during the ERICA training cohort 1 and cohort 2 in six European countries (the UK, Finland, Germany, Italy, Poland, and France). The feedback was gathered by trainers and delivered via e-mail to the development team. The checklist was modified and finalized based on feedback for the online application. The development process is presented as (Supplementary Material Figure  S2 : Outline of the modified checklist development process used in developing the Family Needs Checklist). The technical development process of the mobile application was implemented by two data professionals at Tampere University of Applied Sciences and made available in the Google Play Store.
Going through this section is voluntary. It is meant to be informative only. The statements section, with binary yes/no options, consists of evidence-based known CM risk factors as presented above. After completing the checklist, the feedback section includes general positive feedback if there are no risk factors found. Concerning the risk factors found, the parent or caregiver will get research-based information about each CM risk and protective factors as well as contact information and online links or phone numbers to support services. As a result of the development process, the FNC mobile application is available for free for validation in different cultures. The Family Needs Checklist mobile application is available in the Google Play store in German, Italian, Finnish, and English languages.
Data Source and Ethical Approval The method used in this study have been already described in other publications aimed at assessing the impact of lockdown on other drug classes in the Tuscan general population  [23, 24]. In this observational retrospective study, the healthcare administrative database (HAD) of Tuscany was used, covering approximately 3. 6 million inhabitants. The HAD contains data on healthcare services accesses in the regional area, reimbursed by the Italian Healthcare System (NHS) to all regional citizens. The HADs consulted for this study included: (i) the archive of demographic and administrative data of individuals living in the catchment area who receive national healthcare service assistance; and (ii) the pharmacy claim registry that provides information (i. e. , dispensing date, number of packages, substance name, anatomical therapeutic chemical code [ATC] and defined daily dose [DDD]) on all community prescription reimbursed by the NHS  [25].
Strengths and Limitations An important strength of this study is that data related to an unselected population from one of the largest Italian regions, which accounts for over over 3. 5 million of inhabitants. This study has some limitations. First, in Italy both ADs and anxiolytic drugs (i. e. , benzodiazepines) are indicated for the treatment of the mental health symptoms after traumatic events. However, in Italy only ADs are fully reimbursed by the NHS and thus captured in the HAD, while the cost of anxiolytics is totally charged to patients. As a consequence, data about benzodiazepines use is generally not available in the Italian HAD. Moreover, the study did not include information about drug indications because in Italy this information is not available in the HAD. Additionally, for the second lockdown we included only six data points, which is slightly lower than the minimum number of points (eight) required to have robust estimates, therefore the results on the second lockdown should be considered with caution.
Using machine learning techniques, we then develop a methodology that allows implementing the skills taxonomy in online vacancy and applicants' data, thus capturing both the supply and the demand side. Implementing the methodology with Uruguayan data from the job-board BuscoJobs, we assign skills to 73% of applicants' employment spells and 91% of vacancies. We consider this a successful implementation since the exploited text information often does not follow a standardized format. The advantage of our approach is its reliance on data that is currently available in many countries across the world, thereby allowing for country-specific analysis that does not need to assume that occupational skills bundles are the same across countries. To the best of our knowledge, we are the first to explore this approach in the context of an emerging economy.
For this purpose, we take our taxonomy to the data, exploiting information from the Uruguayan job board BuscoJobs. BuscoJobs is a private job-search portal supported by the Ministry of Labor and Social Security in Uruguay. Our data include the entire content uploaded between 2010 and 2020, capturing both the supply side (through applicants sharing their current profile and labour market biographies) and the demand side (in the form of vacancies posted by firms). Exploiting rich open text descriptions of job ads posted by firms and employment histories of applicants, we pre-process the text, distil and analyse the keywords and expressions that pertain to a given skill, using machine learning techniques (i. e. , a natural language processing (NLP) model). To preview our empirical results, we find that our NLP model performs well with regards to the number of observations (vacancies and applicants' job spells) that we can characterize in terms of skills 2 Our taxonomy is complementary to the global skills framework developed in  ILO (2021).
At the same time, we show that in addition to high-skilled work, the data includes meaningful numbers of vacancies requiring and applicants having intermediate and even lower qualification levels. We also establish that the bias in the BuscoJobs data remains stable across time for both vacancies and applicants' job spells, since the discrepancy between the occupational distribution in the BuscoJobs data and the household survey data does change in meaningful ways from 2010 through 2020. This means that the data are suitable for studying dynamics over time (see Deming and Noray 2020; Hershbein and Kahn 2018 for a similar argument). This study advances the literature on skills dynamics in emerging and developing countries. While the relative scarcity in empirical evidence in these economies is in large part due to the absence of appropriate data, some studies have addressed this data challenge in innovative ways.
2020; Spitz-Oener 2006). Moreover, skills variables in the PIAAC and STEP surveys are limited in number, making it difficult to assess skills in a comprehensive manner. This stands in contrast to the granularity of the data that we propose. This paper is structured as follows. Section 2 develops the skills taxonomy and explains how we classify unique skills within categories and subcategories. Section 3 describes the BuscoJobs data. Section 4 summarizes our methodology to implement the skills taxonomy in the BuscoJobs data. It describes the process we follow to create skills variables through the identification of keywords and expressions and the use of machine learning techniques. Section 5 concludes with a brief discussion of how online job data could be used in the future in emerging and developing countries.
Section 3. Data and descriptive statistics To implement our skills taxonomy empirically, we rely on online data from the Uruguayan online job To post online vacancies through BuscoJobs, enterprises need to create an account on the portal and pay a small fee. The portal offers different types of enterprise subscriptions, which cover different time spans and allow firms to publish a distinct number of vacancies (going from three to 600 vacancies, depending on the length and the type of subscription). All subscriptions enable firms to view users' CVs for the duration of the chosen subscription. Given the information that firms provide in the vacancies they post, the database contains rich information on firm characteristics, industry, vacancy requirements in terms of education and experience, and vacancy characteristics such as working conditions.
As a distinguishable feature of BuscoJobs, the job board does not only offer the possibility to firms to post vacancies, but also allows jobseekers to register and apply for these vacancies directly through the portal. Jobseekers can create an account for free. They include in their profile basic personal information, information about their educational attainment, their entire history of employment experiences, the technical skills they possess, and the languages they master. This allows us to observe rich longitudinal data on individuals' characteristics and employment biographies. Jobseekers can also express their job preferences, whether they are actively seeking a job, whether they are employed or unemployed and whether they want their profiles to be fully visible by employers. 21 Finally, the applicants include their past work experiences and ongoing employment spells.
This is detailed in an open text format, which is important for us to characterize the skills associated with these spells, according to our skills taxonomy, as well as to create the ISCO occupations' variables. Another advantage of the BuscoJobs database, in comparison to other job boards and job aggregators in the country, is that BuscoJobs does not include duplicate job postings and presents a much lower volatility over time (Equipos Consultores 2020). Most importantly, as mentioned above, BuscoJobs covers a comparatively long time series from 2010 through 2020, and captures vacancies, applicant employment biographies, and applications. This stands in contrast to other major job boards and aggregators in Uruguay and other data sources that are prominently used in the literature, such as the Burning Glass data, as these only contain vacancy data.
Second, online vacancy and applicants' data is typically collected at high frequency, making it possible to avoid time gaps associated with more common data collection methods, such as survey data, which are sampled in (sometime long) intervals. Given the "real-time" character of online data, some of the delays associated with the processing and cleaning of survey data can be avoided (ILO 2020), in particular if the initial database associated to an online job board or job aggregator is well structured. One of the major drawbacks associated with the use of data derived from online job boards and aggregators is their representativeness. As these data are not based on random sampling techniques, it is difficult to draw generalised conclusions about the whole universe of firms or the overall working age population, as the data tend to be biased towards certain segments of the labour market, in particular higher-skilled occupations  (Cedefop 2021; Hershbein and Kahn 2018).
BuscoJobs applicants' data The BuscoJobs applicants' database has 666,797 user profiles, out of which 388,041 include information related to their previous work experience, which is fundamental for our analysis. Since we could construct the detailed employment biographies of these individuals, our overall sample size consists of 1,231,555 job spells. As shown in Figure  1 , we have good data coverage for all years between 2010 and 2020 and observe that the number of individuals joining the job board tended to increase between 2010 and 2017, to slightly decrease again until 2020. 24 The Uruguayan informality figure is based on the household survey introduced in the following sub-section. The definition of informal job follows the ILO harmonized approach and may therefore deviate from national estimates.
Figure. Absolute number of individuals joining BuscoJobs in a given year, 2010-2020 Notes: We exclude a small share of individuals joining the job board before 2007 (i. e. , the year when BuscoJobs was first created), since our data cover all profiles that were active between January 2010 and December 2020. The applicants' database provides rich information about applicants' personal characteristics (e. g. , sex, birth dates, and places of residence), as well as previous work experience (e. g. , job spells' dates, position, location and a detailed open-text description of the position), and their education background. As mentioned above, applicants can also indicate the skills developed in each job spell and can participate in the soft skills test provided by d'Anchiano, the results of which appear on the general profile of each user and are available to us.
Furthermore, prior to the process of recognizing and classifying skills through our taxonomy, a classification of occupations was carried out through a machine-learning approach, where we created variables that further characterized employment spells in terms of the associated 2-digit ISCO-08 occupation (more details on the Machine Learning approach are provided in Section 4). This occupation variable, along the skills variables created, will allow us to capture skills compositions within occupations categories, and assess the representativity of the data in terms of the occupational distribution. To assess the representativeness of the database, we compare selected characteristics of the job board's users with information from the Uruguayan household survey, the Encuesta Continua de Hogares.
25 We mostly rely on data for 2020 for our analysis, unless a variable of interest is not available, in which case we refer to 2019 data. 26 Within the applicants' data, 54. 6 percent are women, which is in line with estimates for the workingage population in Uruguay, of which 52. 2 percent were female in 2020. We also note that 66. 5 percent of the applicants were located in the Montevideo area in 2020, compared to 41. 3 percent of the overall labour force. The overrepresentation of the Montevideo area in the BuscoJobs applicants' database is likely related to the different job-search methods between large metropolitan areas, such as Montevideo, and smaller urban areas or rural areas. This is consistent with the previous finding of the higher relevance of print media and word of mouth in rural areas (ILO 2020).
Indeed, with 53. 6 percent a comparatively large share of those declaring to have used the internet to find a job were living in the Montevideo area in 2020 27. In terms of the age distribution, which is illustrated in Figure  2 , the applicants' data contains a disproportionately high share of younger applicants. This discrepancy is the most pronounced among individuals aged 25-29, who account for 26. 2 percent of BuscoJobs users in 2020, compared to nine percent of the Uruguayan working-age population in the same year. More similar representations are observed for the age category 35-39, that account for 12. 5 percent of BuscoJobs users and 11. 3 percent of the working-age population. The tendency is inverted as older age categories are taken into account. The larger share of young portal users is intuitive given their stronger familiarity and use of IT tools, compared with older workers.
It can also be rationalized by younger workers entering the labour market for the first time, and thus searching more actively for employment. However, it is somewhat difficult to put our findings into perspective, since previous studies using online applicants' data do not provide similar age information. 28 Marinescu and Skandalis (  2021 ) are an exception, reporting an average age of 31. 8 for individuals using the online job-search platform of the French PES. This is even slightly below the average age we find (33. 6 years). Other studies directly focus on young individuals, perhaps because these are frequent users of online job-search engines when applying for jobs  (Barbarasa, Barrett, and Goldin 2017; Kureková and Žilinčíková 2018). When looking at educational attainment, we find that applicants using the BuscoJobs job board have a higher level of education compared to the Uruguayan labour force.
Namely, 27. 3 percent of BuscoJobs applicants 29  possess an undergraduate degree and 32. 3 percent a graduate/technical degree. Meanwhile, according to data from the Uruguayan household survey in 2019 (latest year with available data), 12. 3 percent of the labour force had achieved the first degree of tertiary education, and 2. 3 percent had completed a degree of the secondary stage of tertiary education 30. In terms of occupational distribution, Figure  3  shows a certain divergence when comparing BuscoJobs applicants' employment spells in 2020 with the national estimates for the same year. Notably, a disproportionately large share of the BuscoJobs users was employed in a clerical support profession (34. 8 percent), while none of the users indicated any work experience in an agricultural profession, meaning that this category is completely absent from the database.
This suggests that when analysing the applicants' data, it may be appropriate to focus on certain groups of workers that are well (2018) show that there is significant overlap (but also some divergence) between the online data from the job board CareerBuilder and national survey data for the United States. 31 While the applicants' dataset is not fully representative of the overall working-age population, it has several comparative advantages. The dataset includes reasonably large samples for occupations requiring intermediate or lower levels of formal qualification, such as services and sales workers, clerical support workers, and even workers from elementary occupations. This suggests that the data can be used for analyses that extend beyond highly skilled workers, which one might not have thought at first glance 32.
Moreover, as mentioned above, the database contains rich information and its granularity - 31 The authors report a correlation coefficient of 0. 71 between the two distributions. Note, however, that their reference group are jobseekers, i. e. , they do not use the national employment distribution as a benchmark, as we do. 32 For example, Deming and Noray (2020) focus on college graduates, while  Deming and Kahn (2018)  focus on professional jobs, noting that these are particularly well represented in online data. These authors match job vacancies from Burning Glass with survey data sources. in particular the precise information on tasks performed on the job by individuals -is an asset to obtain a better understanding of skills dynamics. Although BuscoJobs' applicants are disproportionally concentrated among younger and more educated jobseekers in the Montevideo area, looking for jobs in clerical and technical occupations, the distributions are stable across time as illustrated in Figure  4.
BuscoJobs vacancy data BuscoJobs is one of the leading job boards in Uruguay. According to the provider's calculations, it captures around 50 percent of online vacancies in Uruguay (BuscoJobs market statistics shared with the authors), providing information that is detailed and reliable: e. g. , vacancies are recent and have high turnover and most of the fields available for each vacancy are complete (Di Capua, Queijo, and Rucci 2020). The job adds posted are unique and are clean from repetitions, which is not a given among job portals. As other job portals and job aggregators in Uruguay post the same vacancies several times, it is estimated that the effective coverage of BuscoJobs is in fact closer to 60 percent of online vacancies. We restrict our attention to vacancies that refer to jobs located in Uruguay and the period from 2010 to 2020.
This results in a total of 86,966 vacancies, posted by more than 6,000 firms. It is important to  The job board allows capturing very detailed information associated with each job advertisement. This includes characteristics of the firm posting the vacancy (e. g. , name and location), characteristics of the vacancy (e. g. , desired age of the candidate and sex 33 , work experience expected, and the wage range offered for the advertised position) and a detailed description of the job advertised, along with the educational and the skills requirements. Furthermore, BuscoJobs generated upon request a variable indicating the economic sector to which an enterprise is associated, following the ISIC Rev. 4 classification at the 4-digit level. For this, the BuscoJobs data were matched to the administrative database of enterprises produced by the country's statistical institute INE (Instituto Nacional de Estadísticas) with information until 2017 34  , using unique firm identifiers.