dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset to be filled Missing LE data stemmed from (1) total shutdowns of flux stations following battery discharges or vandalism acts, (2) malfunctions of KH20 krypton hygrometers after precipitation events when air humidity permeated the sensor because of seal degradation, and (3) rejection of LE data identified as class V data by  ST and ITC tests (Sect. 2. 5. 3). Table  3  displays the number of available data derived from EC measurements over the three fields, when considering Table  3. Summary of the available latent heat flux (LE) data derived from the eddy covariance measurements conducted on each of the three fields: dates of the beginning and ending of measurement periods, number of total daytime data over 30 min intervals for calculating the fluxes, numbers and percentages of data belonging to quality control classes (I-IV: good quality data; V: rejected data), numbers and percentages of the missing data due to dysfunctions of the KH20 sensors, and numbers and percentages of missing flux data due to total shutdowns of the flux stations.
Rationale in choosing and implementing gap-filling methods Amongst the existing LE gap-filling methods listed in the Introduction (Table  1 ), we selected some methods that differ in the use of ancillary information, either meteorological variables or energy fluxes. The meteorological data to be used were those provided by the meteorological station, while the flux data to be used were those collected at each of the three flux stations of interest (Sect. 2. 3). We did not select methods that involve measurements of soil water content or vegetation canopy since energy fluxes indirectly account for the latter at a spatial scale closer to that of the LE missing data (see results about footprint analysis in the last paragraph of Sect. 2. 5. 3). Amongst the existing LE gap-filling methods listed in the Introduction (Table  1 ), we selected the commonly used REd-dyProc method that relies on lookup tables (LUTs) and mean diurnal variation (MDV) to fill missing flux data with those collected under similar meteorological conditions or with averaged values over adjacent days.
We also selected methods that fill LE gaps by using multilinear regressions on other energy flux data (Rn, H , and G). We did not select methods based on artificial neural networks because ensuring the relevance of calibration, testing, and validation steps requires large datasets of at least 1 year  (Abudu et al. , 2010; Beringer et al. , 2007; Eamus et al. , 2013; Papale and Valentini, 2003).
REddyProc For the REddyProc method, we selected the online tool available at https://www. bgc-jena. mpg. de/bgi/index. php/Services/REddyProcWebRPackage (last access: 1 April 2018), and that is based on  Reichstein et al. (2005). The REd-dyProc method combines the covariation of the convective fluxes with meteorological variables  (Falge et al. , 2001b)  and the temporal autocorrelation of the convective fluxes  (Reichstein et al. , 2005). Gaps are filled in accordance with available information by considering three cases: (1) solar radi-ation (Rg), air temperature (T air ), and vapor pressure deficit (VPD) data are available; (2) Rg data only are available; and (3) none of the Rg, T air , and VPD data are available. For Case (1), the missing LE value is replaced by the average value under similar meteorological conditions within a time window of ±7 days.
LE reconstructed from evaporative fraction Evaporative fraction (EF) is defined as the ratio of LE over available energy (Rn -G) when assuming the latter equals the sum of convective fluxes (H + LE). Li et al. (2008)  and  Shuttleworth et al. (1989)  showed that EF was almost constant during daytime hours. Although rebutted  (Hoedjes et al. , 2008; Van Niel et al. , 2011) , various studies stated that EF at midday (EF md ) is statistically representative of daily EF, and thus recommended using EF md for estimating LE  (Crago and Brutsaert, 1996; Crago, 1996; Gentine et al. , 2011; Li et al. , 2008; Peng et al. , 2013). The estimation of missing LE data was twofold. In a first step, EF md was calculated on a daily basis by using the measured data over the 4 h centered on solar noon, provided that at least 75 % of the eight 30 min data were available between noon -2 h and noon +2 h for LE, Rn, and G.
Tailoring the gap-filling methods to the conditions of hilly crop fields The gap-filling methods were tailored to the conditions of hilly crop fields by splitting the dataset on the basis of the airflow inclination that is driven by the combined effect of Table  4. Splitting of the dataset into three periods when implementing the LE-Rn and MLR gap-filling methods. The three periods are labeled green vegetation (GV), pre-senescent vegetation (PS), and senescent vegetation (SV). They are indicated along with the vegetation and climatic conditions. GAI stands for leaf area index of green leaves; ET 0 stands for the reference evapotranspiration. Minimum and maximum GAI values are averaged values over the three fields A, B, and C. Cumulative precipitation, mean ET 0 , and mean air temperature are derived from measurements at the meteorological station.
wind direction, topography, and vegetation height. The analysis of the experimental conditions showed that the wind regimes were typified by two main wind directions, i. e. , northwest and south, which induces upslope and downslope winds on fields A and B (Sect. 2. 6. 1). Therefore, any of the three datasets for fields A, B, and C were split into two sub-datasets that correspond to northwest and south winds. We recall that (1) northwest winds correspond to downslope and upslope winds on fields A and B, respectively; (2) south winds correspond to upslope and downslope winds on fields A and B, respectively; and (3) field C was horizontal.
Main Most existing gap-filling methods for LE measurements include a prior splitting of the time series to be filled (Table 1), so that missing data are filled with existing observations collected under similar conditions. REddyProc relies on time windows up to 280 days, and the EF method relies on a daily time window. For both the LE-Rn and MLR methods that assume stable regressions among energy fluxes, we split the dataset into three periods that differed in vegetation phenology. This accounted for changes in soil water content and vegetation height at monthly to seasonal timescales. The beginning and end of each period are given in Table  4 , along with the vegetation and climatic conditions. The first period corresponded to active green vegetation, with moderate reference evapotranspiration, and with abundant and frequent precipitation events that supply plant transpiration and soil evaporation.
Acknowledgements. This study was supported by the  IRD JEAI JASMIN-Tunisia  project (INRGREF, INAT, and LISAH), the  IRD/ARTS program , the  MISTRALS / SICMED  project, the  Agropolis Foundation  (contract  0901-013 ), and the  ANR TRANSMED   ALMIRA  project (contract  ANR-12-TMED-0003-01 ). The  ORE OMERE  is thanked for providing the meteorological data. We also thank the  International Joint Laboratory NAILA. We are extremely grateful to  Tim McVicar  and to the anonymous reviewers for constructive discussions that helped to improve the paper. Edited by:  Marina Díaz-Michelena Reviewed  by: two anonymous referees Geosci. Instrum. Method. Data Syst. , 7,  151-167 , 2018 www. geosci-instrum-method-data-syst. net/7/151/2018/N. Boudhina et al. : Evapotranspiration over hilly crop fields.
(2002) Bosque del -Extremely stable/NR b significantly different from 0 Apache NWR -Time window (daily basis) Hui et AmeriFlux -Three sites/hilly topography/forest (deciduous, Imputation methods Good gap-filling performances. The al. (2004) coniferous, subalpine) methods performed similarly; multiple -NR/NR imputation method is easily portable in -No split (1-year dataset) the context of worldwide networks. Alavi et NR -One site/flat/winter wheat Kalman filter Good gap-filling performances of al. (2006) -NR/NR Multiple imputation (MI) Kalman filtering approach with -Time windows according to the used method Mean diurnal variation (MDV) smaller errors than the other methods. (1 year/4-15 days/±10 days/±10 days) Multiple regressions Roupsard et NR -One site/flat/coconut plantation LE = a Rn + b NR al.
(2006) -NR/NR MDV to gap filling H -Time window (1 month) Beringer et FLUXNET -One site/flat/woodland and open forest savanna Feed-forward back propagation NR al. (2007) -NR/NR (BPN) artificial neural network -NR (ANN) (Papale et Valentini, 2003) Zitouna- ORE OMERE -Three fields/hilly/winter cereals LE = a Rn + b Good gap-filling performances when Chebbi (2009) -Neutrality or low instability/externally driven discriminating between the two -Based on upslope/downslope airflows prevailing wind directions. (1-year dataset) Abudu et Bosque del al. (2010) Apache NWR -One site/NR/salt cedar trees -NR/NR -Random split for calibration/testing over X % of existing data with X =.
To facilitate the hackathon, three organizations from different sectors with diverse expertise joined forces: the H3Africa Bioinformatics Network (H3ABioNet), the University of Notre Dame, and IBM Research Africa. H3ABioNet  (Mulder et al. 2016 ) is a Pan-African bioinformatics network composed of 32 bio-informatics research groups distributed among 14 African countries and forming part of the larger H3Africa initiative (The H3Africa  Consortium et al. 2014) , IBM Research Africa is one of the first private industrial research organizations in Africa, and the University of Notre Dame is an academic and research university in the United States. The University of Notre Dame is leading an effort to support a Dialogue on Reverse-Engineering Assessment and Methods (DREAM)  (Stolovitzky et al. 2007 ) challenge on the prediction of artemisinin sensitivity of malaria parasite isolates using transcriptional data sets.
The DREAM challenges are a series of annual crowdsourcing challenges during which biological data sets are released to the international community to build computational models that address specific biological questions  (Stolovitzky et al. 2007). In contrast to typical crowdsourcing challenges, the University of Notre Dame, IBM Research Africa, and H3ABioNet collaborated and gathered researchers with different levels of expertise to assess the idea of opening up research data sets prior to publication for use in a data mining workshop. Such a forum, which we refer to as the DREAM of Malaria Hackathon, provides a number of benefits over the conventional research process: (1) opening up research data prior to publication to independent researchers provides an opportunity for unbiased assessment and critique of study design and data quality; (2) unpublished research data coupled with specific scientific questions can be used to train young scientists on hypothesis generation and technical skills for real-world data analysis problems; and (3) unpublished research data, when used in such a setting, exposes young scientists to the possibility of participating in scientific discoveries.
For the majority of the participants, this was the first time they had ever taken part in a hackathon, and it highlighted to them the importance of multidisciplinary collaborations. From an educational point of view, knowledge transfer is an important part of these data analysis-based hackathons enabled by working with peers across domains  (Lapp et al. 2007; Groen and Calderhead 2015; Nandi and Mandernach 2016). It offers participants a new and interesting education and learning environment, especially for PhD students and early career researchers, by providing the opportunity to learn how to contribute to collaborative scientific projects. In order to assess knowledge transfer outcomes at the hackathon, all participants were asked to fill in a survey focusing on the various skills required for the data analysis during the hackathon.
Despite these challenges, some post-hackathon activities have continued, albeit at a slower pace. Although not every hackathon participant is still actively contributing, the findings led the data generators to obtain new data that will be used in the open innovation challenge, and the hackathon outcomes are evolving continuously and influencing the organization of the challenge. Some of the findings were presented at the Sage Assembly and DREAM RECOMB meeting in 2017. Funding for hackathons involving participants from various countries puts limitations on when and how often such hackathons may be organized. Fortunately, in the case of this hackathon, funding was available through H3ABioNet to support travel and accommodation of all participants, and IBM Research Africa provided a meeting venue and collaboration spaces.
The relationship between H3ABioNet and the University of Notre Dame was bolstered by the commitment of the data generators in its grant proposal to the NIH to make the data available prepublication for an open science challenge. This contrasts the common model of data sharing in which data are only shared with a large group after data generators have published the results. Hackathon leaders were selected from the three organizations. For H3ABioNet leaders, junior academics were selected who had demonstrated leadership skills and would benefit from the experience of managing the project. H3ABioNet agreed to support the recruitment of participants across its nodes in several African countries to participate in the hackathon. An official call for applications for hackathon participants was placed online in June 2016 and was open for about 3 wk.
Forty-eight applications were reviewed by a committee from H3ABioNet. Online meetings took place before the hackathon to initiate the discussion between the participants and to develop a roadmap along with an initial analysis pipeline. The main advantage of this pre-hackathon phase was that the teams became familiar with the data sets and proposed a consensus direction as a team, which facilitated the work at the beginning of the hackathon. Several collaboration tools were used, including MConf for video conferencing, Slack, Synapse, and Google docs. A summary of the main hackathon goals and components, including communication platforms, is shown in Figure  6.
Data sets The University of Notre Dame generated several microarray data sets consisting of whole-genome expression for 20 P. falciparum  isolates coming from Southeast Asia. The scientific goal of this hackathon was to predict the sensitivity of various P. falciparum isolates to dihydroartemisinin (DHA) based on genome-wide gene expression profiles of the isolates obtained at two developmental time-points in the presence of DHA or under control conditions. Two proxies were used to measure per-isolate sensitivity of the drug: the dichotomous fast/slow clearance status of an isolate (slow-clearing isolates display some resistance to the drug, whereas fast-clearing isolates respond to the drug as expected) and its IC 50 drug concentration (measuring the amount of drug to be administered to halve the parasite load).
The data generators also provided metadata on the samples, including the geographical source of the malaria parasites, presence of specific mutations at key genetic loci associated with the emerging artemisinin resistance in Southeast Asia, experiment batch information, etc. All the data were stored on the Synapse platform  (Derry et al. 2012)  and were accessible only to participants. It is important to note that the hackathon organizers undertook steps to ensure that the data set used in the hackathon remains confidential until the DREAM crowdsourcing challenge is launched. First, the data are not publicly available and access was only provided to hackathon participants. Second, a separate set of data that will be used for blind testing of models in the DREAM challenge was not available at the time of the hackathon and will be inaccessible even to the hackathon participants.
Furthermore, it fostered communication, teamwork, and presentation skills among participants at different levels of their careers. Hackathons can be useful throughout the life cycle of projects, e. g. , at the start of projects for skills development and transfer, before data are generated in order to have computational pipelines in place for processing the data, midway through a project to derive a common version of processed data for analyses by a consortium, and toward the end of the project for analyzing and comparing results and sanity checking. Another post-project application of hackathons could be to replicate results from a publication analysis. H3ABioNet is using hackathons as a means to build and subsequently utilize capacity in bioinformatics in Africa, and to provide a dedicated environment for hackathon goals to be realized.
Acknowledgments The generation of data used in this hackathon was supported by an  NIH  grant ( R21  grant  AI103872-01AI ) to M. T. F. ( University of Notre Dame, IN, USA ). H3ABioNet was supported by the  National Human Genome Research Institute (NHGRI)  and the Office of The Director (OD),  National Institutes of Health  under award number  U41HG006941. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. IBM Research Africa hosted the conference at its research facility. Katie Button-Simons  and Sage Davis were supported by the  Eck Institute for Global Health.
2000b)  or preXPR2  (Madzak et al. 1999) , some previously described, like hybrid preLIP2  (Gasmi et al. 2011; Gasmi et al. 2012; Ledesma-Amaro et al. 2015)  or insect-derived preSoAMY  (Celińska et al. 2015) , or novel, previously undescribed SPs in the context of recombinant protein secretion in Y. lipolytica. The novel SPs were identified through genomic DNA data mining, and their secretory capacity was assessed experimentally in comparison with known secretory tags. We took advantage of Golden Gate approach, for construction of expression cassettes, and micro-volume enzymatic assays, for functional screening of large libraries of recombinant strains. Based on the adopted strategy, we identified novel secretory tags, characterized their secretory capacity, indicated the most potent SPs, and suggested a consensus sequence of a potentially robust synthetic SP to expand the molecular toolbox for engineering Y.
In silico analyses Genomic DNA sequence of Y. lipolytica CLIB122 used in this study can be acquired from GRYC database (http://gryc. inra. fr/). Amino acid sequences of AEP and LIP2 N-terminal polypeptides are available in GRYC database or Nucleotide database at NCBI (https://www. ncbi. nlm. nih. gov/). Function of proteins encoded by the sequences serving as the SPs donors was determined using GRYC database or Nucleotide database at NCBI. D score values, discriminating signal peptides form non-signal peptides based on probability of the presence of a signal peptidase cleavage site, as well as the primary amino acid structure of the SPs were predicted using SignalP 4. 1  (Petersen et al. 2011 ) (http://www. cbs. dtu. dk/services/ SignalP/) and PrediSi  (Hiller et al. 2004 ) (http://www. predisi.
When optical density at 600 nm reached 0. 6, ColE9 (300 nM) in combination with Polymyxin B (144 nM; Sigma-Aldrich) or EDTA (1. 5 mM; Sigma-Aldrich) were added to the media. Optical density was then monitored every 30 min for 4 h. In parallel, samples from treated and untreated cells were transferred in microscopy chambers between an agar pad and a coverslip. DIC images of individual cells were taken every 30 min for 1. 5 h to check for growth and division defects. Cell length was defined as the distance from one pole of a bacterium to the other and measured as a straight line on at least 30 cells per condition using ImageJ software. All experiments were done in triplicate. All curves and scatter plots were constructed using GraphPad software (Prism V). SOS stress response assays consisted of growing E.
After a further wash step in PBS, bacteria were incubated for 30 min at room temperature with donkey anti-rabbit antibody AF555 (Invitrogen) at 25 µg/ml final concentration in PBS. Cells were then washed twice in PBS and mounted between an agar pad and a coverslip for confocal microscopy acquisition. FRAP acquisition. Measurements were taken using a Zeiss LSM 780/Axio Examiner Z1 motorised upright laser scanning microscope equipped with Argon multiline 458/488/514 nm (25 mW), and HeNe 561 nm (1 mW). Optical magnification was provided by a ×100 oil-immersion objective (Zeiss, NA 1. 4). Images were recorded by scanning the laser over a 13. 5 × 13. 5 μm area with the image size set to 512 × 512 pixels, scan speed set to 7 (3. 15 μs per pixel) and a digital zoom of ×10. Images were recorded using Zeiss Zen 2011 software.
1% of maximum power. Images were analysed using ImageJ software (version 1. 48; Wayne Rasband, National Institutes of Health, Bethesda, MD). TIFF stacks were created from lsm files obtained from Zeiss Zen 2011 and if required, aligned either manually or by using the stackreg plugin for ImageJ if a second cell was present. TIRFM acquisition. Live cell single-molecule tracking was performed at room temperature on a custom-built TIRF microscope built around the Rapid Automated Modular Microscope (RAMM) System (ASI Imaging). Laser lines of 488 and 561 nm provided by a multi-laser engine were used to activate GFP and TMR/ mCherry, respectively (iChrome MLE, Toptica). Laser beams were collimated at the fibre output and focused (×100 oil-immersion objective, NA 1. 4, Olympus) onto the sample at an angle allowing for highly inclined thin illumination  38, 39.
Spherical aberration was minimised by choosing an immersion oil with a refractive index giving symmetrical point spread functions and image stacks of several μm thickness taken with 0. 125 μm z-steps and 15 images (three angles and five phases per angle) per z-section and a 3D-structured illumination with stripe separation of 213 nm and 238 nm at 488 nm and 594 nm, respectively. Laser power of 10% was used for both laser lines and the sample scanned over 4 µm thickness (32 slices). Image stacks were reconstructed using Deltavision softWoRx 6. 1. 1 software with a Wiener filter of 0. 002 using wavelength specific experimentally determined OTF functions  40. This leads to a halving of the pixel size and a doubling of the X and Y direction pixel number (ie, four times as many pixels), but no change in z-steps in the image stacks.
In addition, the mean intensity of the ROI in the post-bleached images was normalised to the mean ROI pre-bleach intensity. Background fluorescence (mean fluorescence outside of cells) was also used for 'double normalisation'  41. More than 30 cells were analysed in this way per condition, in triplicate. All TIRFM videos were analysed using custom software (PATRACK), implemented in visual C ++ and provided by Milhiet and Dosset  42. The centre of each fluorescence peak was determined with sub-pixel resolution by fitting a two-dimensional elliptical Gaussian function. The two-dimensional trajectories of single molecules were constructed frame-by-frame, selecting particles that displayed a single bleaching step. Diffusion coefficient values were determined from a linear fit to the mean square displacement (MSD) plots between the first and fourth points according to the equation: MSD(t) = 4Dt.
By contrast, >95% of free (ie, non-coupled) fluorophores at a similar density would share <3 pixels if distributed randomly. CoLocalizer Pro 2. 7. 1 software (CoLocalization Research Software, http://www. colocalizer. com) was used for co-localisation of red and green channels for TIRFM. The co-localisation value corresponded to the ratio between yellow pixels over the sum of yellow, red and green pixels on each overlapped picture. TIRFM images resulted from 100 consecutive frame stacks in both channels and 2D SIM images resulted from the slice displaying maximum cell length (defined as the middle of the bacterial Z plane). Experiments were done in triplicate, with at least 30 cells per condition. Distribution of fluorescence intensity along the x-axis of bacteria was determined using a custom script implemented in MATLAB (version 2012a, MathWorks)  16.