dataesr/scientific-paragraphs-categorization huggingface dataset:
From 2018 forward, the matching to the ISIC Rev. 4 was done manually using the economic activity reported by firms when registering in BuscoJobs. Finally, as for the applicants' data, we created a variable capturing occupational categories, following the 2-digit ISCO-08 classification and using a machine-learning approach (see again Section 4 for more details). For the assessment of the representativeness of the BuscoJobs online vacancy dataset, we focus on the year of 2020and again compare selected summary statistics to those derived for the same year from the Uruguayan household survey. It should be noted that this comparison is indicative only, given that the online job vacancy data and household survey data are collected in an inherently different way. Vacancy data depend on turnover rates in the labour market, which may differ across economic sectors, while the household survey data represent a snapshot of workers' characteristics in a given moment.
Nevertheless, this comparison yields meaningful insights to assess the representativeness (or lack thereof) of the data. 35 irst, we note that 61. 3 percent of the vacancies are located in the capital of Montevideo in 2020 36  , compared to 41. 3 percent of the overall labour force. Moreover, the data derived from BuscoJobs online vacancies include a disproportionally high share of high-and medium-skilled professional categories, and underrepresents low-skilled occupations, when compared to national labour market 33 More than 25 percent of the vacancies indicate a preferred sex of applicants. Roughly two thirds of the job postings indicating a preference for female candidates are associated with occupational categories "4 -Clerical support workers" and "5 -Service and sales workers". Job postings indicating a preference for male candidates are more evenly spread across occupational categories, with a slightly higher incidence within the category "9 -Elementary occupations".
estimates (see Figure  6 ). With 86 percent of all online vacancies, the broad ISCO-08 categories 2-5 (i. e. , professionals, technicians and associate professionals, clerical support workers, and service and sales workers) dominate in the BuscoJobs data. This suggests that the data allow for meaningful analysis especially of these groups of workers, which cover a significant share of the overall working-age population (54 percent according to the household survey). Low-skilled occupations, captured by the broad category 'elementary occupations', are underrepresented in the BuscoJobs data, although they still account for 5. 6 percent of the observations (compared with 18 percent in the household survey). Furthermore, skilled agricultural occupations are completely absent from the BuscoJobs online vacancy database, while they account for 4.
6 percent of the occupations observed in Uruguay. Overall, these patterns are not surprising as national employment distributions across occupations deviate in similar ways from other online vacancy sources studied in the literature. This includes the BurningGlass data, which capture vacancies for professional jobs in a more comprehensive way than other types of vacancies  (Deming and Kahn 2018; Hershbein and Kahn 2018; ILO 2020). While the BuscoJobs vacancy data are not fully representative of the Uruguayan labour market and tend to overrepresent high-skilled workers, they nevertheless cover a meaningful number of medium-skilled, and even some low-skilled jobs. Similar conclusions arise when looking instead at the distribution across industrial sectors (see Appendix Table  A1 ). As discussed above, we again look at changes in the representativeness of the BuscoJobs vacancy data over time to ensure the internal validity of temporal analyses.
Reassuringly, Figure  7    the skills taxonomy and BuscoJobs data in an efficient way: • Translation of keywords and expressions: As explained in Section 2, our skills taxonomy defines fourteen sub-categories through keywords/expressions that we identified from the existing literature on skills dynamics. Since our online data are in Spanish, we first translated the keywords/expressions from English. • Text normalization: As is typical for data analyses of this kind (see, e. g. ,  Gentzkow, Shapiro, and  Taddy 2019), we normalized the text through text mining techniques, mainly using the Natural Language Toolkit (NLTK) 39 library in Python. This step was similarly performed for the relevant variables in the BuscoJobs data and for the list of keywords/expressions in the skills taxonomy. It included: (1) lowercasing of capital letters; (2) "unidecoding" to simplify characters and delete accents that are used in Spanish (for example, changing "á" to "a"); (3) eliminating all characters that are not actual text (for example, "NaN" or "Null" values, which are the common representation of missing data in Python); (4) eliminating stop-words, which are commonly used prepositions and conjunctions that do not provide useful information by themselves (for example, the Spanish equivalents of "the", "a", "of", and "in") 40 ; and (5) eliminating other concepts that do not add value, such as names of months, days, cities and countries and single letters.
We manually identify and consider other (Spanish) versions of the same concept, such as "Análisis Predictivo de Datos" or -after applying the text-normalization described before -"analisis predictivo datos". Once we carry out the tokenization, a total of seven elements are added to our list: the tokens "analisis", "predictivo" and "datos"; the 2grams "analisis predictivo", "analisis datos" and "predictivo datos"; and the original 3-gram "analisis predictivo datos". In this way, we give our taxonomy greater power to capture relevant concepts within the free text descriptions of vacancies and applicants' job spells. The tokenization is carried out using the NLTK library but requires a manual revision of the results. For example, for "programming language" (included in the skills sub-category of "software (specific) skills and technical support") we kept the token "programming" but deleted the token "language", as the latter does not pertain to software skills and would have thus induced an error in the classification.
With the text organized, we proceeded to create the skills variables: • Initial variable creation: For the creation of the skills variables, we coded indicator variables for each of the fourteen sub-categories of skills, which take the value of one whenever a relevant token or n-gram was identified in the BuscoJobs data. We also coded related variables capturing how many times a relevant keyword/expression appeared in the data, as a proxy for skill intensity. • Refined variable creation using synonyms: We further expanded the initial list of keywords by also accounting for their synonyms. This step allowed us to increase the probability of capturing a given skill. Manually, we added relevant synonyms that we identified based on our work with the BuscoJobs data and previous knowledge. We also used an automated webscrapping method that targeted the website www.
As part of process (i), we created a dictionary of keywords and expressions with high frequency using the free texts of both the vacancies and applicants' job-spells variables, to which the corresponding ISCO-08 code was assigned. In specific situations, this required manual classification. Then, this dictionary was paired with the pre-processed texts achieving the classification based on the text matches. Regarding process (ii), we observed approximately 5,000 observations already classified at the 4-digit level by BuscoJobs in each of the databases. Based on this and process (i), we trained a predictive model. After several tests, we concluded that a two-stage model would be the best approach. In the first stage, the model was able to predict the ISCO classification at the one-digit level, and the.
second stage was used for the classification model at the 2-digit ISCO level. The strategy for training the model was also included various steps: Reducing the databases to those observations when the dictionary-based model differed from the BuscoJobs' classification; separating this subset into a training sample (66%) and a test sample (33%); and searching for the best combination of model and parameters according to the results. Ultimately, we tested three models to process the text columns: Random Forest, Support Vector Machine, and Gradient Boosting. Through a comprehensive testing strategy of different combinations of hyperparameters and various cross-validations for each combination, we chose Gradient Boosting to code the 1-and 2-digit ISCO 08-occupations in the vacancy data and Random Forest for the applicants' data.
Thus, the Danish NRC conducts a surveillance of candidemia  (8)  and azole-resistant Aspergillus fumigatus  (9). Others survey candidemia through nationwide surveillance  (10)  or multicenter surveys in Germany, Belgium, Spain, or Bulgaria  (11). Specific investigations are punctually led by the European Centre for Disease Prevention and Control  (12). Many studies rely on passive notification  (13, 14) , but other sources of information are available. Bitar et al. reported an overall incidence of 5. 9/100,000 cases/year based on the French National Hospital Discharge Database (2001 to 2010), with a global mortality rate of 27. 6%  (15). More recently, Webb et al. analyzed the Intermountain Healthcare Enterprise Data Warehouse (2006 to 2015) and described an incidence of IFDs (excluding Pneumocystis jirovecii pneumonia [PJP]) of 27.
7%] versus 476/8,443 [5. 6%], P , 0. 0001). Among the 10,886 episodes of IFDs, fungemia accounted for almost one-half of them (n = 5,363, 49. 3%), followed by PJP (n = 2,106, 19. 4%), invasive aspergillosis (IA; n = 1,661, 15. 3%), mucormycosis (n = 314, 2. 9%), cryptococcosis (n = 255, 2. 3%), fusariosis (n = 254, 2. 3%), endemic mycoses (n = 96, 0. 9%), and other IFDs (n = 837, 7. 7%) (Fig. 1B ). Concurrent infections, mostly IA/mucormycosis (n = 109), IA/PJP (n = 104), and IA/fungemia (n = 93), were diagnosed in 4. 3% (n = 470) of the cases. Mixed species were recorded in 342 cases with no change over time (data not shown) and identified mostly during fungemia (4. 4%) and IA (2. 8%). There was a significant decrease in the proportion of IA recorded (from 17. 7% to 13. 6%, P = 0. 0007), but all other IFDs remained stable (Fig.
DISCUSSION We showed here the potential of a university hospital's network based on active and perennial collaboration with well-trained mycologists and infectious disease clinicians for surveying IFDs. The IFDs recorded through RESSIF include fungemia but also more challenging diagnoses, such as invasive mold infections or PJP. Of note, each Even if medical specialties are not distributed evenly across centers altering the distribution of populations at risk of IFDs, we can reasonably expect that the sustained surveillance and the aggregation of data collected in 29 academic centers totaling almost 11,000 cases smoothened the center and even geographical effects. The design of the RESSIF network allowed us to describe the landscape of IFDs in France between 2012 and 2018 and their own dynamics.
jirovecii DNA loads should not be neglected  (36). Using the RESSIF data to extrapolate incidences at a national scale is challenging. Since RESSIF encompassed 45% of national capacity of university hospitals in terms of hospitalization days, it could be done for IA, mucormycoses, and PJP; these IFDs occur mainly in immunocompromised patients mostly treated in university hospitals. Fungemia however can occur in nonimmunocompromised patients in hospitals not included in the RESSIF network for a lack of a referent mycologist. According to the administrative data for France, a mean of 1,556 candidemia/year was recorded between 2001 and 2010, leading to an annual incidence of candidemia of 2. 5 to 3. 5/100,000 population/year  (15). Here, we collected in average 737 cases/year suggesting that we missed at least 53% of the candidemia knowing that indeed the figures are probably higher with the increase of the French population and its aging.
Overall, it is likely that the IFDs burden in France is more than twice what we recorded in RESSIF since we also excluded IFDs following surgery, such as fungal peritonitis  (37) , and those for which clear criteria are still missing, such as in patients with chronic respiratory diseases. Even though RESSIF was designed to overcome limitations of surveillance programs based on administrative data, it still has some, as follows: (i) it does not cover the entire country (see above with the estimations at a national level) as opposed, for example, to the Danish survey on candidemia (8); (ii) despite providing clinical data which are usually missing in other surveillance systems, global and attributable mortality analysis would have required additional information, such as preemptive and prophylactic therapies, therapeutic follow-up, and severity grading for ICU patients, which cannot be collected reliably unless specific studies are set up; and (iii) the electronic case report form (e-CRF) includes predefined variables potentially preventing the recording of new events.
Interactions with our colleagues and SPF permit uncovering such cases. Altogether the e-CRF flexibility and interactions with SPF allowed to rapidly evaluate the Saprochaete clavata outbreak in 2012  (38) , to assess the small number of Candida auris in France  (12) , or more recently to investigate the burden of IFDs in the context of COVID-19  (39). The e-CRF is indeed easily amendable through the VOOZANOO platform to record new parameters (e. g. , COVID-19 context). (iv) The last issue concerns the completeness of the recordings for a lack of a double check with the hospital databases. We regularly assessed the reporting evolution for each CC-NCRMA and challenged suspicious annual variation. However, to consider the possible weariness of some participants that could lead to incomplete reporting in the future, we are planning to reduce the e-CRF.
Participating centers and individuals had no financial reward. Information system VOOZANOO and database organization. The real-time Web-based electronic case report form (e-CRF) was developed using VOOZANOO, an open-source platform to rapidly create questionnaires and surveillance applications (http://www2. voozanoo. net/). The database is saved daily on a server approved for medical data storage. The architecture is hierarchical allowing the administrator (NRCMA) to define levels (NRCMA/city/hospital/ward), and roles with specific rights (referent mycologist and collaborator/clinician) for each account. The rights concern the files (creation, update, deletion, and download) and the tools (creation or simple use of queries and variables available for comparison). Each CC-NRCMA has full access to its own data, is free to perform any in-house study, and can request access to part of the whole data set  (32).
The coordinating committee, including representatives of CC-NRCMA and NRCMA, oversees validation of the projects. An annual meeting held at Institut Pasteur allows representatives to share updates on the network and results of substudies. Questionnaire. Data on the patient (gender, date of birth, ward, underlying conditions, immunosuppressive drugs, foreign devices, and travel history) and on the episode of IFD (signs, symptoms, imaging, means of diagnosis, direct examination, culture, histology, PCR, antigen detection, specimens studied, prior antifungal prescription, and coinfections), initial therapy within the first 48 hours (antifungals and surgery), and outcome at 3 months (dead, alive, or lost) with date of last news/death were recorded. Most variables were categorical with unique or multiple choices or drop-down menus to facilitate secure recording and analysis.
Some free text entries allowed one to add relevant information. Missing or ambiguous information for outstanding data and failure to send the isolate triggered queries (through e-CRF, mail, or phone call). All edits were traced. A unique "case identifier" allowed one to link episodes for a given patient. Strain identification. The CC-NRCMAs are responsible for fungal identification. Nevertheless, they agreed to send their isolates for central characterization at the NRCMA, except for the common species (Candida albicans, Candida glabrata, Candida tropicalis, Candida parapsilosis, Candida krusei, Candida kefyr, and Aspergillus fumigatus), unless the isolate exhibited an unusual antifungal susceptibility profile. The NRCMA provided polyphasic identification based on morphology, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF; Bruker biotyper, Germany) mass spectrometry, DNA barcoding  (41) (42) (43) , and antifungal susceptibility testing based on the EUCAST broth microdilution method  (42, 44).
Active Surveillance of Invasive Fungal Diseases in France mBio Month YYYY Volume XX Issue XX 10. 1128/mbio. 00920- 22 12  Downloaded from https://journals. asm. org/journal/mbio on 04 May 2022 by 157. 99. 174. 50. Data analysis. Each record was assigned to one of the six main exclusive categories of risk for IFD (using the following hierarchy: malignancy, solid organ transplantation [SOT], recent surgery [,1 month] excluding curative management of the IFD, HIV infection, and other conditions known as risk factors for specific IFDs [e. g. , diabetes, corticosteroid therapy, and systemic inflammatory disease]) or none known. Complex cases were reviewed by two NRCMA staff members. IFDs were recorded as invasive aspergillosis (IA), PJP, mucormycosis, cryptococcosis, fusariosis, endemic mycosis, fungemia (if not included in the specific IFDs listed), or other IFDs (rare localizations due to common species and infections due uncommon species not listed above).
IA cases diagnosed only by positive PCR tests were included, anticipating the new EORTC/MSGERC criteria  (2). Even if they were not yet approved, we used the same approach for mucormycosis. For PJP, classification relied on microscopy and PCR results, as follows: proven with positive microscopy and probable with negative microscopy and a positive qPCR. Global mortality was assessed 1 and 3 months after diagnosis. Data were analyzed anonymously using Stata/SE 15. 1 for Mac (StataCorp. , College Station, TX). To avoid autocorrelation, characteristics of the patients, including death, were analyzed for the first episode recorded for the global analysis and for the first episode of the corresponding IFD for the analysis of specific IFDs. Trends and incidence were calculated for the 21 CC-NRCMA perennials between 2013 and 2018.
Incidence rates were calculated per 10,000 hospitalization days using annual hospital activity data (SAE administrative data, Ministry of Health, https://www. sae-diffusion. sante. gouv. fr/sae-diffusion/recherche. htm). Official data on the French population were available online (https://www. insee. fr/fr/statistiques/2381476). Their evolution was analyzed by linear regression. The trends of specific parameters were analyzed using the chi-square test for trends. Comparisons were based on chi-square or Fisher's exact test when needed for discrete variables. Ethical considerations. The research was carried out in compliance with the French law and the Declaration of Helsinki (as adopted in 2000). The surveillance of the NRCMA was approved by the Institut Pasteur Institutional Review Board 1 (2009-34/IRB) and the "Commission National de l'Informatique et des Libertés" according to the French regulation.
ACKNOWLEDGMENTS This work was supported by recurrent financial support from  Santé Publique France  and  Institut Pasteur. The funders had no role in study design, data collection, analysis, interpretation of data, or the decision to submit the work for publication. We thank  Didier Che  ( Direction des Maladies Infectieuses, Santé Publique France ) for his continuous support and interest in invasive fungal infections and  Etienne Sevin  ( EpiConcept, Paris, France ) for his contribution in the development of the RESSIF website using the VOOZANOO platform. We are thankful for the technical help of  Catherine Blanc ,  Anne Boullié ,  Cécile Gautier ,  Virginie Geolier , and  Damien Hoinard  ( Institut Pasteur ) for the characterization of the isolates received at the NRCMA. F. Dromer  and S.
B. analyzed the data, wrote the manuscript, and arranged the figures and tables with input from K. S. , M. D. -O. , D. G. -H. , and  O. L. K. S. curated the entire database. M. D. -O. and D. G. -H. provided expert identification of the isolates. All authors collected data, provided resources, participated in data curation, and/or contributed to the revision of the manuscript. We declare no conflict of interest. The  French Mycoses Study Group  includes collaborators who contributed to these data by their involvement in the management of patients, expertise in diagnostic tools,.
Cosmic Muon Images is a muon tomography citizen science framework developed within the EU-funded (GA-872859) REINFORCE project [1]  (REsearch Infrastructures FOR Citizens in Europe). REINFORCE aspires to decrease the knowledge gap between research and society. Citizen scientists will engage in the process of scientific discovery by analyzing detector data from four major physics domains. Muon Tomography, Gravitational Waves, Neutrino Astronomy, and High Energy Physics join forces with people from all over the world to do science and discuss detection techniques, signal versus background discrimination, and environmental effects on measurement and broadcast the impact of fundamental research on everyday life and on societal progress. In this effort, we want to reach the broader audience possible, and this means also making our data accessible to as many people as possible.
SonoUno [2] is a user centered software developed within REINFORCE that allows people with different sensory styles to explore scientific data, both visually and through sonorization. Cosmic Muon Images utilizes the Zooniverse [3] website to bring muon tomography to the general public and the citizen science community. Muon telescope data are visualized with 3D and 1D plots with the goal being the identification of patterns through a series of lines and points on these plots. The results of this pattern identification will be used to train machine learning (ML) algorithms to discriminate between signal and background events. We will then evaluate the performance of these ML algorithms to more traditional track reconstruction and event selection algorithms that are already in use. The key factor is how they compare with respect to speed and pattern recognition accuracy.
Furthermore, a large dataset cataloged by the eye will act as a stepping-stone toward other studies like background identification and Monte Carlo simulation development. Citizen scientists will benefit through their exposure to calibration and detection techniques for muon telescopes to make their scientific contributions more robust. This will also provide a more fulfilling experience by learning new stuff which is for the most part reserved for higher education audiences. They are going to learn about the different applications of muon tomography in fields like volcanology, archeology, civil engineering, hazard monitoring, and others. A great effort is made toward the inclusion of school students through a series of schools and seminars coorganized together with other EU-funded projects (e.