dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, the results were better than those obtained with the 422-strain panel (Tables  7  and 8 ), with a higher percentage of results reaching the thresholds (from 3. 30% increase in percentage of accepted identification with B1 to 24. 94% increase with D4 when selecting the 1. 7 threshold) and a reduced number of misidentifications. For example, using the in-house database, the species PPV for the D1 algorithm with a 1. 7 score threshold on the comprehensive 422-strain panel increased from 0. 89 to 0. 97 when testing a sub-panel including only the 280 strains for which species were represented by at least three distinct strains. Similar trends were observed when using the commercial database (increase in the species PPV from 0. 72 to 0. 93). Twenty-two species (180 isolates) of this study are represented by at least three references in both databases.
We performed a per species comparison of the efficiency of the two databases for those 22 species, using the D1 algorithm. Results are compiled in Table  9. For the total of the 180 isolates, the percentage of accepted identifications is lower with the Bruker database than with the in-house database (47. 22% vs 91. 67%). The comparison of the species PPV obtained with both databases highlighted weaknesses for four species (A. alternata, F. solani, P. chrysogenum and S. apiospermum) with the in-house database (due to misidentifications with closely related species), and for five species (A. parasiticus, F. proliferatum, S. brevicaulis, T. interdigitale and T. rubrum) with the Bruker database. The species PPV was equal to 1. 00 and identical between the two databases for 13 species of this group.
Discussion As shown in this study and several others, MALDI-TOF MS has recently been optimized to identify filamentous fungi at the species level, provided that an appropriate database is available  [2, 4, [6] [7] [8]. The assessment panel used in this study comprised isolates belonging to 126 % accepted: percentage of submitted strains that fulfilled the criteria for identification at either the genus or species level depending on the applied threshold; species PPV: positive predictive value at the species level; genus PPV: positive predictive value at the genus level different species, including many difficult-to-identify isolates. Consequently, the identification rate was low compared with previously published studies, especially studies utilizing the commercial database. Using such a challenging panel, it was possible to define a threshold discriminating correct identifications at the species level from false identifications at the genus level, although identifying a threshold that differentiated between a correct and false identification at the species level among closely related species was not feasible.
Our findings indicate that the best identification results (PPV at the species level and percentage of accepted identifications) were obtained when applying a decisional algorithm in which only the highest score of four spots was taken into account, and the identification was accepted if the score was at least 1. 7. This threshold is notably lower than that recommended for bacteria identification, and has already been advised by several authors for yeast identification  [32, 33]. Furthermore, we also observed a marked decrease in percentage of identification between a threshold of 1. 7 and 2. 0. When the threshold was set too high, we noted a 20% loss in identification efficiency and only a small benefit of 2% in the differentiation of certain close species. Even if the Bruker commercial database yielded a markedly lower percentage of accepted identifications (probably due to the fact that the database was built using liquid culture media, that is does not frequently involve more than two or three references per species, and that there are errors of reference labeling), the PPV remained correct for the spectra reaching the 1.
Interestingly, raising the identification score threshold to 2. 3 did not entirely resolve this identification issue of taxonomically close species, as six strains were incorrectly characterized despite a score above 2. 3, even using our extensive in-house database. Thus, in our study, one could question the relevance of capacity and necessity to distinguish between these closely related species as none of the misidentifications implied a change in treatment. Our results also show that the key step in efficient identification was to increase the number of replicate spots per strain. Testing four spots of each filamentous fungus yielded better identification performance, with an increase of 5% to 7% of accepted strain identifications. Such spot replications did not markedly increase the workload, as it took only a few seconds to perform the additional spots.
However, the replicates may increase the per-strain acquisition time in the laboratory workflow. The four-spot Table  9  Comparison of the identification efficiency of the two databases per species, using the D1 algorithm of identification with a 1. 7 LS threshold, for 180 strains that are represented by three or more strains in both reference databases Nb of isolates : number of isolates from the panel of strain that belong to each species. % accepted: percentage of submitted strains that fulfilled the criteria for identification at either the genus or species level; species PPV: positive predictive value at the species level method has been applied in the routine workflow at the Marseille Mycology Laboratory for the past five years, which demonstrates that this procedure is easily applicable in hospital laboratories to identify filamentous fungi isolated from clinical samples and from hospital environment samples.
The multiplication of strains per species as well as subcultures per strain allowed us to cover the variations seen within the environment for filamentous fungi. Our in-house reference database, which yielded markedly better results compared with the commercial Bruker database, was constructed mainly with collection strains and with some clinical strains isolated in our laboratory and obtained during routine analyses. These clinical strains were later included in the BCCM/IHEM collection in Brussels. Nevertheless, even our enhanced database still requires optimization to identify rare species. Indeed, the poor representation of rare species in the database may explain the identification percentage of 87. 4% obtained with this challenging panel of 422 strains (92. 1% with the 280 strains represented by more than three strains in the reference database), compared with the extremely good identification results of common species (98.
Conclusion The growing number of publications on MALDI-TOF MS-based fungal identification indicates that this technology is widely used and applicable to the routine laboratory workflow. MALDI-TOF MS-based identification of fungi yields exceedingly more accurate results compared with morphology-based analyses  [21]. Furthermore, this technology is less expensive and easier than the current DNA based-identification gold standard, with a turnaround time allowing achieving the analysis more rapidly. Currently, the major limitation of this approach is the lack of a comprehensive and efficient reference spectrum library. We acknowledge that in-house reference databases that are non-open-source, such as ours, are of little benefit to the global scientific community. Therefore, we plan to deploy an accessible version of our database along with an identification algorithm that can be queried online to assist scientific teams to expand the identification of fungal species.
The topic of sustainability has mostly been tied to economic development and environmental protection, yet not much attention has been paid to the need for high problem-solving capacity as this underlines both issues. One of the most straightforward ways to improve problem-solving capacity is to nurture 'productive and enduring', i. e. , sustainable, scientific communities. The study examines the co-authoring behaviors of 412 Vietnamese social scientists over the 2008-2017 period via social network analysis to determine if these researchers have formed sustainable scientific communities, using Scopus data. The dataset provides an insightful look into the predominant form of collaboration, i. e. , co-authorship, within the Vietnamese social science research communities. Through basic network metrics such as density and clustering coefficient, the study hypothesizes that the socially sustainable research communities are those with low clustering and high density.
Attribute data We set out to collect the data on Vietnamese researchers working in social sciences and humanities who have published in Scopus-indexed journals. There are three reasons we chose Scopus. Firstly, the Vietnamese government has used Scopus-indexing as a criterion for science funding since 2008  [78]. Second, substantial parts of Scopus data are free and transparent; one can access Google Scholars to look up the publications and use open data sources such as Scimagojr and Scopus to check the journals. And finally, Scopus indexed nearly 22,600 active titles, almost twice the number of its counterpart, Web of Science  [79, 80]. Next, the criteria of for a researcher to be considered a legitimate subject for data collection were determined. The criteria are: a. A researcher must be a Vietnamese national b.
He or she satisfies either or both of these two conditions: • Have been affiliated with a Vietnamese institution; • Have published at least one paper on a social scientific issue related to Vietnam. Being clear on the criteria, we then set out to collect attribute information of the researchers that satisfy the requirements. The result of the data collection process is a complete dataset of 412 scholars' details, consisting of: (i) age, sex, region; (ii) affiliations; (iii) fields of study; (iv) the number of publications in Scopus, (v) the number of research years since the Master graduation; (vi) the number of researchers they collaborated with; (vii) whether or not they have the title of "Professor/Assoc. Professor". To make sure the data is reliable, the research team collected data from various sources such as personal and institutional websites of scholars, journals' websites, Google scholars, and Scopus database.
Then by comparing information in these online sources (Google scholar versus Scopus, personal sites versus organizational websites, etc. ), we were able to eliminate potential errors. For instance, different versions of a Vietnamese name can result in two IDs for one person, or a person has Vietnamese name but is not a Vietnamese national. In the end, the clean set of data constitutes the "Nodes list" ("20170725_net412_ NODES. csv"), which contains the attribute information of each author.
3. 1). two authors appear together in a paper, it is counted toward the "weight" of the link. Figure  2  shows an example of how the edges list is derived from the original data. As an example, in the first row of the table on the left side, a published paper being co-authored by scientists ID s004, s076 and s079 is registered. Then, on the right side, the co-authorship links among these three scholars are documented; and the weight is the count of how many times each pair co-authors. The data was then processed and analyzed using statistical software R (v3. The table describes how relational data is created from table (a) (Adapted from  [17] ). The data for Net1 to Net20 were manually extracted from the full dataset. Nodes lists and edges list for these 20 networks were built by picking relevant edges and nodes from the original lists; all of which are available in the folder 20 Networks' Data.
Standard Network Metrics and Visualization First, it would be useful to visualize the co-authorship connections that exist among all 412 Vietnamese social scientists in our sample. All 412 nodes and their edges are plotted on a graph (called Net412) using "iGraph" package in R (v3. 1. 1), as shown in Figure  3. As node size represents the total articles a researcher has published, interesting patterns emerge. In most of the sub-networks or research communities, the most productive researchers seem to always be central to many different connections. Lying in the top corner of the circle in both figures are 125 researchers who either work alone or work with foreigners. The middle of the circle is occupied by small groups of researchers: size 2-9, which takes up about 40% of the total population.
In the lower corner, there reside the large groups, comprising at least 10 members per group. These groups account for about 30% of the population. Having glimpsed the structure of the network of 412 Vietnamese social scientists, one can then examine the question of social sustainability in more detail by looking at the properties of each scholarly community. Next, 20 communities with at least 5 members each are manually extracted from the total dataset (the data files for these communities can be found in the folder "20 Networks' Data" and "R-commands and figures for all nets"). Each of these subgroups is considered a network in and of itself. In Table  1 , the standard network metrics of these networks are summarized. In Table  1 , each network of Vietnamese social scientists is represented by a series of different numbers.
To aid our understanding, four most noticeable communities are chosen and plotted in the next two figures: one with highest number of nodes and one with highest number of links in Figure  4 ; one with highest number of mean publications and one with lowest number of global clustering coefficient in Figure  5  (The R commands for Figures  4  and 5  and the figures for all the nets that do not appear in the final paper can be seen in Dataset 6, 7 and 9). In Figures  4  and 5 , node size represents the number of publications; color is based on gender, blue for female and red for females; edges are links connect the nodes. In Figure  4 , Net8 and Net20 are compared, as they are quite comparable in terms of nodes and links. In terms of clustering and density of connection, Net6 is higher than Net8 in both aspects; 0.
Conclusions The study has developed a novel way to understand co-authorship behavior in Vietnam's social scientific communities as well as to evaluate their social sustainability. Using social network analysis, the study is able to address the two research questions posed in its premise. First, the study uses the basic network metrics, namely clustering coefficient and density of connection, and extracts 20 communities with at least 5 members each from the total dataset to analyze the extent of social sustainability in Vietnamese social scientists' communities. The data shows there is no group with a high mean of publications, high density, and low clustering. As our initial assumption is that groups with high density and low clustering are sustainable, the data suggests a lack of social sustainability among research networks in Vietnam.
Supplementary Materials: All data sets are available from Mendeley Data repository and open to the public at doi:10. 17632/f3j6rn362v. 2  [84]  with the detailed explanation as follows: Dataset 1: "Folder Net412's Data". This folder contains two files: "20170725_net412_NODES. csv" and "20170729_net412_LINKS. csv". The former lists all 412 individuals in the study and their attributes, each individual is considered a node (vertex) in the network. The latter lists the number of co-written articles between all 412 authors of the network, where relevant; each collaboration is counted as a link (edge) in the network. Dataset 2: "Folder 20 Networks' Data". This folder includes all nodes lists and edges lists of 20 small networks extracted from the original. Dataset 3: "Metrics all nets 20171003.
csv" This dataset contains the summary of all metrics that represent 20 research networks in the study. Dataset 4: "Folder SSD Calculation". This folder contains four files: "Research network details 20171012. xlsx" contains the nodes lists for 20 research networks in the study as well as the calculations for the junior members of each network; "SSD Calculation details 20171012. xlsx" contains the details of SSD calculations for all networks, "SSD Summary 20171012. xlsx" is the summary of final results of calculations for all SSD measurements of 20 networks in the study. Dataset 5: "Folder R-command and figure3" contains R commands and the PDF file of Figure  3. Dataset 6: "Folder R-command and figure4" contains R commands and the PDF file of Figure  4. Dataset 7: "Folder R-command and figure5" contains R commands and the PDF file of Figure  5.
Acknowledgments: We would like to thank  Vuong & Associates  for their research initiative The  Network of Vietnamese Social Scientists ("NVSS") , which enabled the research process and provided the raw data for the study. We particularly thank  Dam Thu Ha ,  Ho Manh Toan  and  Nghiem Phu Kien Cuong  for their excellent research assistance. We thank  Nancy K. Napier  and  Dam Quang Minh  for their valuable comments. We are also grateful for useful comments from participants-who came from different academic disciplines-in the seminar "Beauty and the  Light of Hope " co-organized by  Western University Hanoi and Vuong & Associates in Hanoi , on October 3, 2017, particularly  Nguyen Tu Cuong  (mathematics),  Nguyen Ngoc Chau  (biology),  Pham Duc Chinh  (mechanics),  Khuat Thu Hong  (sociology),  Le Van Canh (linguistics) ,  Nguyen Viet Cuong  and  Tran Quang Tuyen  (economics),  Hoang Anh Tuan Kiet  (physics),  Tran Kien  (law),  Bui Quang Khiem  (fine arts),  Nguyen Pham Muoi  (journalism/media),  Bach Ngoc Chien  (public governance), to name just a few.
Funding Bibhav Acharya, MD was supported by  National Institute of Mental Health (NIMH)  Grant  R25MH060482. Possible's  Mental Health Services program  was supported by the  Nepali government and donors of Possible. It was also supported by an implementation research grant from  Harvard Medical School Center for Global Health Delivery-Dubai  ( 027562-746845-0201 ). Brandon Kohrt, MD, Ph. D was supported by  National Institute of Mental Health (NIMH)  Grant  K01MH104310  and  R21MH111280.
Acknowledgements This work was supported by the  Engineering and Physical Sciences Research Council  [grant numbers  EP/J015849/1 ,  EP/J015296/1 ]; the  EU Marie Sklodowska-Curie ITN-PROMIS  [grant number  641899 ]; the  French "Investment for the Future" Program  [grant number  EquipEx  EXTRA,  ANR 11-EQPX-0016 ]; the  French ANR  [grant number  SUPREME-B ,  ANR-14-CE26-0015 ]; and the  Occitanie region.
14 Information technology can thus be seen as a means for supporting and enabling markets. The "ride sharing" company Uber can serve as the canonical example here. The central element of Uber's business are the apps it proposes to both riders and drivers as well as the data infrastructures that these apps connect to. They match buyers and sellers in physical space, implement a contractual framework through terms of service, handle payment, and constantly monitor participants' behavior through a series of feedback mechanisms. Uber can thereby organize the economic activity of providing personal transportation on demand through a market rather than a firm, at least for the part concerning the actual driving. Agre 15 later significantly extends Ciborra's argument by showing, in detail, how information technology is capable of lowering transaction costs through "capture", that is, through forms of grammatization and disambiguation that apply when activities become mediated by computers.
" Deleuze: Foucault, p. 140, author's translation. 42 While the common translation of "fini-illimité" as "unlimited finity" may be more elegant than " f ini-unlimited", this amounts to a rather drastic change in emphasis. For a discussion of that notion from a different angle see Alex Galloway: Computers and the Superfold, in  : Deleuze Studies, 6/4 (2012), pp. 513-528. 43 Deleuze: Foucault, p. 132. How does the notion of the fini-unlimited lead to a third understanding of order? The crucial element, here, is the idea that a limited number of elements can yield an almost unlimited number of combinations. For anyone acquainted with contemporary techniques for data analysis, this should ring familiar. For any sufficiently complex dataset, the idea that "the data speak for themselves" is nonsensical; we select from a wide variety of mathematical and visual methods to make the data speak, to investigate them from different angles, to answer questions that orient how we look at them.
There is a guiding interest against which the data are made meaningful. A similar argument can be made in reference to what is arguably one of the most important inventions in the field of computing, the relational database model. 45 The central idea informing this model is to cut data into the smallest parts possible to allow for the dynamic recombination of information at retrieval time through a powerful query language that allows for a maximum number of "views". Outputs are ordered in reference to the question asked. To cite another example, machine learning techniques, e. g. Bayes classifiers used for spam email filtering, can be seen as practical techniques for creating such views, models, or combinations: by "showing" the spam filter which mails we consider spam, the classifier "learns" to treat each word or feature in a corpus as an indicator for spamminess.
46 And no two users' classifier profile will be exactly the same, not only because they receive different emails, but also because they will have different definitions of what constitutes an "unwanted" message. In short, the fini-unlimited is the very domain computers excel at. But what does this mean in the context of a discussion of markets? For the liberal economists Foucault discusses, the market is an "unproblematic" concept, not in a political, but in an epistemological sense. A market is wherever there is an encounter between supply and demand, little more to say about it. For 45 Edgar F. Codd: A Relational Model of Data for Large Shared Data Banks, in: Communications of the  ACM, 13/6 (1970), pp. 377-387. 46   the neoliberals, things are more complicated, because the market is under constant threat of "distortion" and the state therefore needs to govern "for the market" to guarantee that it functions well.
| Bimodal respirometry Catfish were fasted for 24 hr prior to measurements, and then, individuals were transferred gently to bimodal respirometers  (Lefevre, Bayley, & McKenzie, 2016; McKenzie et al. , 2015). Four respirometers were partially immersed in two baths of well-aerated water (0. 4 m 2 surface area, 12 cm water depth) with each setup partially screened behind opaque black plastic sheeting so that routine air-breathing behaviours were not inhibited by fear of human presence  (Lefevre, Wang, Phuong, & Bayley, 2011; Shingles, McKenzie, Claireaux, & Domenici, 2005). Water was maintained at 26. 5 ± 0. 5°C using submersible aquarium heaters. Fish were placed in the respirometers in the evening between 18. 00 and 19. 00 hr for respirometry data collected with the animals undisturbed for the ensuing 23 hr.
Data were collected starting from 6 hr after the fish were placed in the respirometers, hence from about 00:30 until they were removed at about 17:30, an interval of 17 hr. The MO 2AIR and MO 2WATER were calculated for this time interval for each individual and summed to calculate routine metabolic rate (RMR). The proportion of RMR derived from air-breathing (%MO 2AIR ) was calculated as the percentage of total oxygen uptake over the measurement interval, for each individual, and was used in subsequent analyses. The standard metabolic rate (SMR) of each individual was estimated by the quantile method  (Dupont-Prinet et al. , 2010; Chabot, Steffensen, & Farrell, 2016). This assumes that a certain proportion of the measures of RMR are below true SMR because of temporal variability and possible measurement errors  (Chabot et al.