dataesr/scientific-paragraphs-categorization huggingface dataset:
For all mass spectrometry experiments (SELDI and MALDI), the spectra were calibrated using a set of reference proteins of known mass. This resulted in only small differences, less than 0. 05%, in the masses observed for the target proteins, for example, the p7878 in all IEX, HPLC and 1D SDS-PAGE fractions displayed mass variations of no more than 63 Da. The masses of the proteins identified by LC-MS/MS were compared in silico to those of peptides in the proteome database for the three complete genomes sequences of S. agalactiae strains A909, NEM316, 2603V/R, and the incomplete genome sequences of five strains (18RS21, 515, CJB111, COH1, H36B). Table  2  shows that the criteria for successful identification (number of different peptides, sequence coverage) are largely fulfilled, and summarizes selected characteristics of the four biomarkers, which were identified as CsbD-like protein (p6258), the small subunit of exodeoxyribonuclease VII (exoDNase VII) (p7878), thioredoxin (p10464), and the L7/L12 subunit of ribosomal protein 50S or RpL7/L12 (p12200).
agalactiae isolates only according to patterns of protein masses  [33, 34]. By contrast, we determined protein abundance as being differently distributed among groups of isolates, and identified the primary sequences of some of these potential biomarkers. The predicted masses of the putative biomarkers are slightly higher than those identified by SELDI (Table  2 ); this may be due to some low-level fragmentation resulting from either the FastPrep sonication of bacteria or proteolysis in the aqueous buffers used. Also, to maximize the purification yield, we used the S. agalactiae isolates with the highest detectable levels of the protein for each of the four biomarkers, but none of these isolates was a reference isolate with a completely sequenced genome. The BLAST data (Table  3 ) indicate that despite the homology, there may be differences in the primary sequences of the proteins produced by different S.
agalactiae isolates. According to the EMBL-EBI database (pfam05532), CsbD is a bacterial protein produced in response to general stress. Its expression in Bacillus subtilis is mediated by sigma-B [s(B)], an alternative sigma factor controlling the general stress regulon  [35]. Sigma-B is activated in response to numerous physical stress stimuli and conditions of energy starvation. The exact role of CsbD in the stress response is unclear. In Escherichia coli, the putative stress-response protein YjbJ, identified by MALDI-TOF-Figure  2. Expression levels of four biomarker proteins produced by S. agalactiae isolates clustered into five groups according to origin/clinical outcome. X -axis: bovine mastitis (red), endocarditis (blue), meningitis (green), respiratory infections (magenta), vaginal carriage (cyan).
Acknowledgments VB is recipient of the  ENP Graduate Program fellowship  from  École des Neurosciences de Paris Ile-de-France  and the  Fonds de Recherche en Santé du Québec (FRSQ)  doctoral award. The original research was supported by the  MD Foundation, Natural Sciences and Engineering Research Council of Canada (NSERC)  and the  Canadian Institutes of Health Research (CIHR).
The directed fishery for orange roughy to the west of Ireland and Britain started in the early 1990s in ICES Division VIa (Fig. 1 ), where it was rapidly depleted and the fishery ceased  (ICES 2011). The a Corresponding author: leonie. dransfeld@marine. ie deepwater mixed fishery continued, however, on other target species. Fisheries moved towards ICES Subarea VII in early 2000 and peaked in 2002 with landings over 5000 t of orange roughy taken in Subarea VII for that year. Thereafter, catch rate data also suggested sequential depletion of local aggregations. A total allowable catch (TAC) was introduced for orange roughy in ICES Subareas VI and VII from 2003 onwards. Spatial management measures were also implemented around the Porcupine Bank. EU TAC was drastically reduced between 2006 and 2009 and has been set at zero since 2010.
VMS analysis In EU waters, all fishing vessels >15 m are required to transmit their position and speed at least every 2 h via Vessel Monitoring Systems (VMS) by satellite  (EC 2003). Spatial and temporal distributions of the French and Irish Deepwater fisheries in the part of ICES Subareas VI and VII that is within Ireland's exclusive economic zone (EEZ), were derived from VMS data between 2006 and 2011. France and Ireland were the two main nations operating deepwater fisheries within the Irish EEZ at this time  (ICES 2012). Irish deepwater fisheries were identified by linking VMS data to logbook data (see  Gerritsen and Lordan 2011)  and selecting vesseldays where the retained catches contained at least 50% deepwater species. Species were classified as deepwater species if they were listed in annex 1 of the European deepwater access scheme  (EC 2002).
French deepwater fisheries were identified by only selecting vessels with deepwater fishing licenses defined by the same regulation. In addition, positions of both data sets were only selected if they fell between the 800-2000 m contour lines. Exploratory survey data prior to the development of deepwater fishing suggest that although orange roughy may occasionally be caught in small numbers shallower than 800 m to the West of the British Isles, only a minor proportion of the population may occur at such "shallow" depths  (Bridger 1978; Ehrich 1983). Abundance increases over the depth range 800-1200 m and abundance reduction from fisheries can be expected to have further reduced occurrence in the non-preferred (including shallow) parts of the species habitat. Fishing positions were identified by applying a speed filter (instantaneous vessel speeds between 1.
Distribution of orange roughy Spatial catch information on orange roughy within the Irish EEZ was compiled from scientific observer programmes, scientific surveys and high resolution catch data based on VMS and Irish logbook analysis. Data from deepwater surveys included the orange roughy acoustic survey programme carried out on Porcupine Bank in 2005, the Irish deepwater trawl survey, which sampled the European shelf slope between 53. 5 and 57 • N and the northern Porcupine Bank between 2006 and 2009, and data from the Scottish Deepwater survey, which sampled the Scottish slope between 55 • N and 60 • N for the years 2000 to 2011. See  O'Donnell et al. (2007) ,  Neat and Burns (2010)  and  Johnston et al. (2010)  for survey details. For the high resolution commercial catch data, daily retained catches of orange roughy from the Irish vessel logbooks were allocated to the fishing positions for each vessel-day (see above for details on VMS analysis); e.
g. , if a vessel on a given date had 10 fishing positions and a daily retained catch of 200 kg of orange roughy, the assumed catch at each of the fishing positions would be 20 kg. Overall, 2340 catch records with positional and depth information were compiled, which included 502 records with orange roughy catches above zero. The catch position data from all years were gridded at a spatial resolution of 1. 8 min longitude and 1. 2 min latitude, corresponding to the same grid dimensions as the VMS data. This provided a single distribution for the species, not a time series. Positional information translated into 1230 grid cells within the Irish EEZ, 355 of which contained positive orange roughy catches.
Productivity susceptibility analyses Two productivity susceptibility analyses (PSA) were carried out following methods described by  Hobday et al. (2007 Hobday et al. ( , 2011) )  using a modified version of the Marine Stewardship Council (MSC) assessment worksheets. The first PSA was a multi-species comparison of species that are typically caught in the mixed deepwater trawl fishery west of Ireland and Scotland (ICES subareas VI and VII) and included five teleosts and three elasmobranchs. Productivity scores were calculated for seven attributes, based on biological data derived from published literature using Northeast Atlantic estimates where available (Table  1 ). Scoring categories were adjusted from  Hobday et al. (2007)  to account for the overall longevity of the species examined and their body sizes (Table  2 ).
In addition to their large body sizes and high longevity, their low reproductive potential increases their vulnerability still further  (Clarke et al. 2003) , which was fully reflected in the multispecies PSA output. The susceptibility of orange roughy to current and historical fisheries was evaluated by carrying out a high resolution analysis of the spatial overlap between the distribution of the stock and the spatial footprint of recent fisheries. For this purpose, every suitable data source for the biological distribution was aggregated over a time period of about 10 years, including data from fisheries-dependent and -independent monitoring programmes. This approach would not be suitable for a highly mobile or short-lived species, as it could not be ascertained whether the species distribution was stable over time.
Experimental Crystal data  Data collection: CrystalClear-SM Expert  (Rigaku, 2009) ; cell refinement: CrystalClear-SM Expert; data reduction: CrystalClear-SM Expert; program(s) used to solve structure: SHELXS97 (Sheldrick, 2008); program(s) used to refine structure: SHELXL97  (Sheldrick, 2008)  and CRYSTALBUILDER  (Welter, 2006) ; molecular graphics: PLATON  (Spek, 2009) ; software used to prepare material for publication: SHELXL97. Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: KQ2001).
We first provide a brief overview of collaborative creation and the different approaches that have been used to create language resources. We then provide details of Phrase Detectives and JeuxDeMots, followed by other notable efforts of GWAP for language resource creation. Next we discuss some key issues in using a gaming approach, focusing on task design, player motivation, and data quality. Finally we look at the costs of each approach in terms of development, distribution and ongoing administration. In conclusion, we summarise the benefits and limitations of the games-with-a-purpose approach.
This approach, called crowdsourcing  [31] , revolutionised the way traditional tasks could be completed and made new tasks possible that were previously inconceivable due to cost or labour limitations. One use for crowdsourcing can be as a way of getting large amounts of human work hours very cheaply as an alternative to producing a computerised solution that may be expensive or complex. However, it may also be seen as a way of utilising human processing power to solve problems that computers, as yet, cannot solve, termed human computation  [72]. Human computation has particular appeal for natural language processing (NLP) because computer systems still need large resources for training algorithms that aim to understand the meaning of human language. By combining collective intelligence, crowdsourcing and human computation it is possible to enable a large group of collaborators to work on linguistic tasks normally done by highly skilled (and highly paid) annotators and to aggregate their collective answers to produce a more complex dataset that not only is more robust than an individual answer but allows for linguistic ambiguity.
Traditional, Entirely Validated Annotation In order to evaluate crowdsourcing approaches to language resource creation it is necessary to also consider more traditional approaches. When we talk about traditional annotation, we think of the methodology used, for example, to create the OntoNotes corpus  8  , containing multilingual annotated news articles, dialogue transcriptions and weblogs, and the SALSA corpus  9  of syntactically annotated German newspaper articles. In this approach, a formal coding scheme is developed, and often extensive agreement studies are carried out. Every document is annotated twice according to the coding scheme by two professional annotators under the supervision of an expert, typically a linguist, followed by merging and adjudication of the annotations. These projects also generally involve the development of suitable annotation tools or at least the adaptation of existing ones.
JeuxDeMots JeuxDeMots (JDM) is a two player GWAP, launched in September 2007, that aims to build a large lexico-semantic network composed of terms (nodes) and typed relations (links between nodes)  [42]  -see Figure  4. It contains terms and possible refinements in the same spirit as WordNet  [21] , although it is organised as decision trees. There are more than 50 different relation types, the occurrences of which are weighted. When a player begins a game, instructions concerning the type of lexical relation (synonyms, antonym, domain, etc. ) are displayed, as well as a term T, chosen from the database or offered by other players. The player has limited time to enter terms which, to their mind, correspond to term T and the lexical relation. The maximum number of terms a player can enter is limited, thus encouraging the player to think carefully about their choices.
A screenshot of the game is shown in Figure  5. The same term T, along with the same instructions, are later given to another player for whom the process is identical. To make the game more fun, the two players score points for words they both choose. Score calculation was designed to increase both precision and recall in the construction of the database  [35]. In the context of the lexical network, precision is related to the set of the most immediate and activated relations of a given term that are uttered by native speakers. Recall is related to the set of the numerous but relevant low activation relations (also known as the long tail)  [43]. The more original a proposition given by both players, the more it is rewarded. Answers given by both players are displayed, those common to both players are highlighted, as are their scores (see Figure  6 ).
For a target term T, common answers from both players are inserted into the database. Answers given by only one of the two players are not, thus reducing noise. The semantic network is therefore constructed by connecting terms by typed and weighted relations, validated by pairs of players. These relations are labelled according to the instructions given to the players and weighted according to the number of pairs of players who choose them. Initially, prior to putting the game online, the database was populated with 140,000 terms (nodes) from French dictionaries, however if a pair of players suggest a non-existing term, a new node is added to the database. Since populating the Fig. 5  Screenshot of an ongoing game in JDM with the target word laver (to wash). Several propositions have been given by the player and are listed on the right hand side.
database the players have added 110,000 new terms however these include spelling mistakes, plurals, feminine forms, numbers, dates and foreign words. In the interest of quality and consistency, it was decided that the validation process would involve anonymous players playing together. A relation is considered valid only if it is given by at least one pair of players. This validation process is similar to the process for indexing images  [73]  and, more recently, to collect common sense knowledge  [45]  and for knowledge extraction  [65]. The activity of the players in JDM constructs a lexical network which contains over 50 types of ontological relations such as generic relations (hypernyms), specific relations (hyponyms), part and whole relations, matter and substance, domain, synonyms and antonyms (the latter also being strongly lexical).
Knowledge Acquisition 1001 Paraphrases  [17] , one of the first GWAP whose aim was to collect corpora, was developed to collect training data for a machine translation system that needs to recognise paraphrase variants. In the game, players have to produce paraphrases of an expression shown at the top of the screen, such as "this can help you". If they guess one of the paraphrases already produced by another player, they get the Fig. 6  Screenshot of the result of a game in JDM. Two words lessive and savon were given by both players for the term laver and hence scores them both points. number of points indicated in the window; otherwise the guess they produced is added to those already collected by the system, the number of points they can win is decreased, and they can try again. Many of the ideas developed in 1001 Paraphrases, and the earlier LEARNER system, are extremely useful, in particular the idea of validation.
Generation A family of GWAP which have been used to collect data used in computational linguistics are the GIVE games developed in support of the GIVE-2 challenge for generating instructions in virtual environments, initiated in the Natural Language Generation community  [40]. GIVE-2 is a treasure-hunt game in a 3D world. When starting the game, the player sees a 3D game window, which displays instructions and allows the players to move around and manipulate objects. In the first room players learn how to interact with the system; then they enter into an evaluation virtual world where they perform the treasure hunt, following instructions generated by one of the systems participating in the challenge. The players can succeed, lose, or cancel the game and this outcome is used to compute the task success metric, one of the metrics used to evaluate the systems participating in the challenge.
Data size and availability In JDM, more than 1,340,000 relations between terms have been collected, the sum of the weights being over 150,000,000. More than 150,000 terms have at least one outgoing relation, and more than 120,000 have at least one incoming relation. The current JDM database is available from the game website. In PD, 407 documents were fully annotated, for a total completed corpus of over 162,000 words, 13% of the total size of the collection currently uploaded for annotation in the game (1. 2M words). The first release of the PD Corpus 0. 1  [58]  is about the size of the ACE 2. 0 corpus 29  of anaphoric information, the standard for evaluation of anaphora resolution systems until 2007/08 and still widely used. The size of the completed corpus does not properly reflect, however, the amount of data collected, as the case allocation strategy adopted in the game privileges variety over completion rate.
Game Interface and Task Design The game interface should be attractive enough to encourage players to start playing and easy enough to use so they keep playing. Before building a GWAP it is essential to have an understanding of game concepts, such as game flow and creating entertaining game scenarios. The design of the task itself will be determined in part by the complexity of the data being collected. By identifying the difficult or ambiguous tasks, the preand post-processing can be improved and the human input can be maximised to produce the highest quality resource possible given the inherent difficulty of the task. Participants may need to be motivated to rise to the challenge of difficult tasks and this is where financial incentives may prove to be too expensive on a large scale. The task design should be streamlined for efficient collection of data as this is one of the simplest ways of reducing costs: by reducing the amount of data needed.
Indirect financial incentives can be a cost-effective way to increase participation in a game. It is common for the majority of the workload to be done by a minority of players. Motivating the right kind of players is a complex issue, central to the design of the game interface and the task, and is as important as attracting large numbers of players because, although collective intelligence needs a crowd, that crowd also needs to do some work. The more a player participates in a GWAP, the more expert they become. A system needs to correlate good performance at the task with good quality data and a ratings system (where the user is regularly judged against a gold standard) is essential to give appropriately challenging tasks. Crowdsourcing approaches with microworking and games tend to be dominated by female participants, although this is not the case for Wikipedia.
Acknowledgements We would like to thank  Jean Heutte  ( CREF-CNRS ) for his help with the concepts of game flow and for the comments of the reviewers of this chapter. The contribution of  Karën Fort  to this work was realized as part of the  Quaero Programme 32  , funded by  OSEO, French State  agency for innovation. The original Phrase Detectives game was funded as part of the  EPSRC   AnaWiki  project,  EP/F00575X/1.
Experimental Crystal data  Data collection: CrysAlis PRO  (Agilent, 2010) ; cell refinement: CrysAlis PRO; data reduction: CrysAlis PRO; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and PLATON  (Spek, 2009) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: TK5222).
Computing details Data collection: CrysAlis PRO  (Agilent, 2010) ; cell refinement: CrysAlis PRO  (Agilent, 2010)  structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and PLATON  (Spek, 2009) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max < 0. 001 Δρ max = 0. 16 e Å -3 Δρ min = -0. 17 e Å -3 Absolute structure:  Flack & Bernardinelli (2000) , 614 Friedel pairs Flack parameter: 0. 014 (15) Special details Geometry. All s. u. 's (except the s. u. in the dihedral angle between two l. s. planes) are estimated using the full covariance matrix. The cell s. u. 's are taken into account individually in the estimation of s. u. 's in distances, angles and torsion angles; correlations between s.
This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test datasets, as well as open-source implementations of the various methods in a common software framework.
We extracted the core profiling methods-namely, the algorithms for constructing per-sample profiles from per-cell measurements-from the larger methodologies, applied them to a typical experiment, and compared their ability to classify compounds into MOA. Our test experiment uses a physiologically relevant p53-wildtype breast-cancer model system (MCF-7) and a mechanistically distinct set of targeted and cancer-relevant cytotoxic compounds that induces a broad range of gross and subtle phenotypes  10. We provide our ground-truth and test datasets and open-source implementations of the methods in order to allow others to readily apply the methods and to extend the comparative analysis to additional methods and datasets.
Sample preparation and image analysis MCF-7 breast-cancer cells were previously plated in 96-well plates, treated for 24 h with 113 compounds at eight concentrations in triplicate, labeled with fluorescent markers for DNA, actin filaments, and tubulin, and imaged as described  10. Version 1. 0. 9405 of the image-analysis software CellProfiler 11 measured 453 features (Table  S1 ) of each of the 2. 2 million cells, using the pipelines provided (Data S1).