dataesr/scientific-paragraphs-categorization huggingface dataset:
The main challenge was that these resources operate from a variety of different perspectives such as protein centric or chemistry centric. The solution proposed was the adoption of a common language to interconnect them. The Enzyme Mechanism Ontology was presented as one option. Kim Pruitt gave the final talk in this session about information flows into NCBI (RefSeq, Gene). Kim talked about the GenBank submission pipeline and how data flowed into RefSeq. An important distinction, which applies generally to biological databases, was made between GenBank which is an archival resource, and RefSeq that is a derived database that can continually improve its records. Another important point was that RefSeq has connections with UniProt that help to reduce duplication of effort, providing a model for other curation resources.
Session 5: Communication and funding The final session covered communication and funding. These two issues had been raised at numerous points throughout the meeting, and the final session gave an opportunity to bring all of these threads of discussion together. This session began with three short presentations. First, Patsy Babbitt outlined some possible directions and points for discussion. Second, Michael Spedding discussed 'IUPHAR, melding and managing complex datasets'. Michael explained the motivation and some history of the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) and described the considerable effort expended together with the community to define a consistent nomenclature for various protein types. The IUPHAR has over 90 committees dedicated to describing a variety of drug target families.
It was felt that the resources were often undervalued given the high level of access and citations. There was discussion on the importance of showing the support of the community for the resources through letters of support for grant funding applications. The biological database community is international, while the grant funding landscape is extremely varied among countries. There was thought to be opportunities for transnational grant funding to support the coordination of clusters of related resources. It was concluded that grant funding or lack thereof was one of the greatest barriers to sustainability in running a specialist protein resource.
2. Users: All databases need users and citations. To gain and keep users, you need to provide query and browsing interfaces as well as someone who answers emails. 3. Befriend Nucleic Acids Research and Database journals: The descriptions of your database are essential to inform new users. But it is also essential to target publications to the readership. 4. Collaborate: collaborators may offer an exit strategy in the future. 4a. Be open: Nobody is going to steal your resource. 5. Give credit: There is more than 100% to go around. 6. Automate: Too much manual intervention makes for an unsustainable database leading to premature death. You need to automate roughly 90% of everything every year. 7. No new standards: Don't invent a new standard. Use what exists. 8. Keep it simple: Google is a model interface.
Acknowledgements We would like to thank  Wellcome Trust Scientific Meetings  for providing funding and logistical support for this retreat. Work on TCDB was supported by  NIH  grants  GM077402  and  GM094610  (to M. S. ). P. G. B. was partially supported by the Project with code  09SYN-13-999  funded by the  European Union  ( European Regional Development Fund-ERDF ) and Greek national funds through the  Operational Program 'Competitiveness and Entrepreneurship ' of the National Strategic Reference Framework (NSRF). The CAZy database acknowledges funds from  Agence Nationale de la Recherche  (grant  BIOMINES  [ ANR-12-BIME-0006 ] and grant  BIP:BIP [ANR-10-BINF-0304 ]). N. S. and R. S. thank the  Department of Biotechnology, Government of India  ( BT/01/COE/09/01 ). N. N. was supported by  Grant-in-Aid for Publication of Scientific Research Results  [248047], organized by the  Japan Society for the Promotion of Science (JSPS)  and by the  Commission for the Development of Artificial Gene Synthesis Technology for Creating Innovative Biomaterial  from the  Ministry of Economy, Trade and Industry (METI), Japan.
The SFLD is supported by grant  NIH   R01 GM60595  to P. C. B. and grant  NSF   DBI1356193  to P. C. B. and G. L. H. Additional support is provided by the  UCSF Resource for Biocomputing, Visualization and Informatics , funded by  NIGMS   P41GM103311  ( T. Ferrin  and P. C. B. ). The ESTHER database acknowledges funds from  Agence Nationale de la Recherche  (grant MolAdCel  ANR-10-BLAN-1216 ). D. J. C. is supported by grants from the  National Health & Medical Research Council (Australia)  ( APP1026501 ,  APP1028509 ) and the  Australian Research Council  ( LP130100550 ). Research on databases at  NCBI-NLM-NIH  was supported by the  Intramural Research Program of the National Library of Medicine, National Institutes of Health  to K. D. P. (RefSeq Database) and D. L. (Histone Database). Work on GtoPdb was supported by  Wellcome Trust  grant ( 099156/Z/12/Z ) (to J.
Transcript profiling was performed using an oligo-based microarray with 75 K probes. Genes linked to characteristic traits of fruit ripening were differentially expressed between climacteric and non-climacteric types, as well as several transcription factor genes and genes encoding enzymes involved in sucrose catabolism. The expression patterns of some genes in PI 161375 fruits were either intermediate between. Piel de sapo and the climacteric varieties, or more similar to the latter. PI 161375 fruits also accumulated some carotenoids, a characteristic trait of climacteric varieties. Conclusions: Simultaneous changes in transcript abundance indicate that there is coordinated reprogramming of gene expression during fruit development and at the onset of ripening in both climacteric and non-climacteric fruits.
As this gene was not present on the microarray its expression was measured by qRT-PCR. Expression of CmACS1 increased up to 10,000-fold during ripening not only in the two climacteric varieties (Ved and Dul) but also in non-climacteric PI (Fig. 6a ). From the    S7 ). Only two ESTs for CmACS5 are present in the International Cucurbit Genomics Initiative (ICuGI; http://www. icugi. org) database, both from fruit tissue from climacteric melon types, suggesting that it is specifically expressed in climacteric fruit types. Four out of the five melon ACO genes annotated in the melon genome were represented on the microarray: CmACO1, CmACO2, CmACO3 and CmACO5. CmACO1 expression increased during ripening in both the climacteric varieties and in PI, but was relatively constant throughout fruit development in PS (Fig.
These two varieties are representative of the two major types of melon in commercial production for the fresh fruit market. The Ved and PS microarray datasets were compared using the Significance Analysis of Microarrays (SAM) tool in the TM4 software package  [66]  and differentially expressed genes are listed in Additional file 11: Table  S8. The data were also analysed using Robin software  [67]  and differentially expressed genes were categorised using MapMan ontology to identify which processes are likely to be transcriptionally regulated during ripening of climacteric and non-climacteric fruits  [68, 69].
There are multiple genes encoding α-galactosidase in melon  [45]  and several were highly expressed in developing melon fruits (cSSH9H24_c, cA_15-C06-M13R_c or cSSH9I1_c), although the predominantly expressed α-galactosidase gene differed between Ved and PS (Additional file 11: Table  S8 ). There were only minor differences in expression of GK, SPS and SPP genes between the two varieties (data not shown). Sucrose can be catabolised by sucrose synthase or invertase. Expression of a CmINV gene (cCL4861contig1), encoding a soluble (vacuolar) acid invertase, was almost 10 times higher in Ved than PS (Fig. 9 ; Additional file 11: Table  S8 ). In contrast, one of the CmCWINV genes (c46d_34-C03-M13R_c), encoding a cell wall invertase, was preferentially expressed in PS. Expression of two CmINVINH genes (cCL2226Contig1 and c15d_02-B02-M13R_c), encoding invertase inhibitor proteins, was about 30 times higher in PS than Ved.
Design and hybridization of the melon NimbleGen® microarray A basic 4-plex melon oligo-based gene expression microarray was designed in collaboration with Roche NimbleGen, based on a previously validated probe set derived from 33,000 melon expressed sequence tags (ESTs) from 12 normalized cDNA libraries, which were obtained from different melon tissues from plants grown under different physiological conditions  [47]. In addition, 244 new unigenes were represented on the microarray, 33 from the GenBank database (http://ncbi. nlm. nih. gov) and 209 ESTs from the International Cucurbit Genomics Initiative database (ICuGI; http://www. icugi. org/). In total, 17,443 melon unigenes were represented on the custom microarray, with four 60-mer oligo nucleotides per unigene, making a total of 75,000 probes  [49, 50].
Microarray data analysis Raw data were analysed without background subtraction using the oligo package from Bioconductor (http:// www. bioconductor. org). Background was corrected by the robust multi-array average (RMA) method. The data were normalized by quantiles, model-fitted using Tukey's Median Polish method and transformed into log 2 values. Differentially expressed genes were extracted using the Bioconductor Microarray Significant Profiles (MaSigPro) R package for the analysis of single and multiseries time course microarray experiments  [51]. MaSigPro follows a two step regression strategy to find genes with significant temporal expression changes and significant differences between experimental groups. The method defined a general regression model for the data. For our experiments with four time points a cubic regression model (degree = 3) was defined.
From analysis of the data with geNorm and BestKeeper software  [85, 86] , CmCYP7 was found to be the most stable reference gene and used as the reference in all samples, as in previous experiments  [47, 87]. Primers for amplification of target and reference genes were designed with Primer Express® Software v2. 0 (Applied Biosystems, Foster City, CA, USA). Primers were based on the 3′-UTR region to ensure target specificity for genes belonging to multi-gene families and are listed in Additional file 4: Table  S3. Intra-assay variation was evaluated by performing all amplification reactions in triplicate. A five-point standard curve was constructed for each gene, including CmCYP7, from serial 10-fold dilutions of cDNA. Efficiency was calculated from the slope of the linear correlation between Cp (crossing point) values of each dilution and the logarithm of the corresponding amount of RNA, according to the equation E = 10 (-1/slope) -1  [88].
The amplification protocol consisted of an initial step at 95 °C for 10 min, and 45 cycles of 95 °C for 10 s and 60 °C for 30s. The specificity of the PCR amplification was checked in preliminary experiments by electrophoresis on an Agilent 2100 Bioanalyzer (Agilent Technologies), and routinely in the main experiments by melting curve analysis and agarose gel electrophoresis. Reverse transcriptase minus controls (RT-) and non-template controls (NTC) were included in each plate to assess the presence of genomic contamination (RT-), and primer dimers and/or primer contamination (NTC). The relative expression of target genes was calculated using Cp values calculated by LC480 software and equation (1): R ¼ N o;target N o;HKG ¼ 1 þ E HKG ð Þ Cp;HKG 1 þ Etarget ð Þ Cp;target ð1Þ where HKG is the reference gene (CmCYP7).
Analysis of differentially expressed genes Genes related to metabolic or signalling pathways of interest were extracted using annotations in the KEGG gene database (http://www. genome. jp/kegg/) [89]  and MapMan ontologies  [68, 90]. Genes that were differentially expressed during fruit ripening in Ved and PS were identified by analysis of the microarray data using the Significance Analysis of Microarrays (SAM) algorithm in the TM4 software package  [91] , and by analysis with the Robin software package  [67]. Data were displayed using MapMan  [68]. For the assignment of melon genes as homologs or orthologs of previously characterized genes in other plant species, we have used plant phylogeny databases as PhylomeDB (http://phylomedb. org/) and PLAZA v3. 0 (http://bioinformatics. psb. ugent.
Acknowledgements We wish to thank  Marta Casado  for assistance with qRT-PCR analysis,  Anna Orozco  and  Rosa Rodriguez  for help with carotenoid measurements,  Kostas Alexiou  for uploading the microarray data at  GEO  and  Walter Sanseverino  for obtaining the correspondence of the microarray contigs with the melon annotated genes. We thank  Beatrice Encke  and  Nicole Krohn  for technical help with the sugar analysis. We also thank  Laura Pascual  for a critical reading of the manuscript. MSa was supported by a  JAE-Doc grant  from the  Spanish Ministry of Science. This work was supported by the  Spanish Ministry of Science and Innovation  ( MICINN  project  GEN2006-27773-C2-1-E  to JGM) and the  German Federal Ministry of Education and Research  ( BMBF  project  0313987  to MSt) within the framework of the  EU Framework 6 ERA-NET Plant Genomics programme.
This paper presents the results of a collaborative work with farmers and a cost-benefit analysis of geospatial technologies applied to irrigation water management in a semi-arid agricultural area in Lower Austria. We use Earth observation (EO) data to estimate crop evapotranspiration (ET) and webGIS technologies to deliver maps and irrigation advice to farmers. The study reports the technical and qualitative evaluation performed during a demonstration phase in 2013 and provides an outlook to future developments. The calculation of the benefits is based on a comparison of the irrigation volumes estimated from satellite vs. the irrigation supplied by the farmers. In most cases, the amount of water supplied was equal to the maximum amount of water required by crops. At the same time high variability was observed for the different irrigation units and crop types.
Generally, the information available to help in the decision process is based only on agro-meteorological data that does not account for the large spatial variability of crop development, soil conditions, and agronomic practices. This work presents the results of a demonstration campaign in the Marchfeld region (conducted in May-September, 2013) and evaluation with users. First, we compared the irrigation volumes estimated from satellite and the irrigation supplied by the farmers to calculate the efficiency of water use. Second, during and after the irrigation season, we assessed the service with the users based on questionnaires and meetings. The results of this qualitative evaluation provided an indication of the usefulness of the information in relation to the user needs. Finally, we developed a cost and benefit analysis and evaluated the willingness to pay for the information generated by the project.
e. , temperature, solar radiation, wind speed, and air humidity) obtained from local weather stations. The ratio of crop ET to the reference ET (ET of the hypothetical grass reference crop i. e. , hc = 0. 12 m; r = 0. 23, and LAI = 2. 88) leads to the crop coefficient (k c ), which is often used in irrigation management. The CWR is calculated by subtracting the rainfall from crop ET. Depending on user requirements, the calculated CWR is further aggregated over different temporal (e. g. , weekly) and spatial (e. g. , individual parcels) scales. The information is then transferred to farmers for irrigation management using webGIS and mobile phone technologies. The most critical point in the data processing chain is the successful acquisition of cloud-free satellite images. A first estimation of the availability of adequate data over our pilot area was carried out using the revisit time map created for the Sentinel-2 mission planning  (Martimort et al.
Based on this strategy, we obtained cloud-free acquisitions every 15 days (average value during the irrigation season) in 2013. An overview of the images acquired in the year 2013 is provided in Figure  2. By using Landsat-8 images in addition to DEIMOS-1 data, substantial cost reductions were achieved. Agro-meteorological variables were measured using wireless weather stations at two different sites located in the southern and northern part of the pilot area (black dots in Figure  1 ). Reference ET was calculated according to the FAO-56 standards and made available twice a day: we provided a first forecast at midday and updated it with the actual value at midnight. In this way, farmers could calculate the water requirement already for the current day. All meteorological data were recorded and stored on a 15min basis; reference ET was calculated on an hourly basis and aggregated to daily values.
Delivery of the Information Experience gained in past projects revealed that decision support tools have to be easy-to-use, reliable and correctly embedded into irrigation routine operations of farmers. Otherwise, it is not possible to achieve successful and effective use of maps and other provided data. Several information technology solutions are possible, based on spatial online data analysis and visualization systems with dynamically interactive graphics display for decision-support or personalized information sheet. We used an existing and consolidated webGIS solution implemented in past projects in Italy in the context of an operative irrigation advisory service (Irrisat) and made available for the purpose of the project. The webGIS system is the main repository for the maps and irrigation advices, which are published on a timely basis, with access restricted to individual farmers for their parcels.
Costs The costs of the service include the initial set-up of the webGIS and farmer's database and the cost for satellite data acquisition, processing and delivery of final maps. These costs can be grouped in two categories: variable costs (that vary with the number of hectares and users served) include the initial farmer's database implementation and a dedicated customer service. Fixed costs, for a given service coverage/area, are the main component of the operative costs and include the satellite data acquisition and processing. To calculate operative cost items we consider three categories: (1) personnel, (2) satellite data, and (3) other costs. Personnel-effort account for the entire satellite processing chain that includes: EO data procurement, atmospheric correction and generation of products, quality control and calibration/validation activities, agro-meteorological data elaboration, and maintenance of geo-infrastructures.
The decision whether to irrigate or not, when and how much, is a critical issue for farmers and it is often based only on personal experience. Monitoring the daily soil water balance is the first steps toward the implementation of these irrigation strategies. The approach necessitates the development of operative frameworks to run a soil water balance model at parcel level and to collect in near-real-time information on irrigation volumes applied by farmers. These technical requirements reflect and complement the requests of the users to adapt the existing webGIS system to (i) manually enter and store in the database the amounts of rainfall and irrigation volumes and use the system as a logbook for their management activities; (ii) access the information via smartphones to save time and have full mobility and (iii) receive alerts via SMS if particular stress conditions are met.
To respond to this request, we developed an application for smartphones based on Sencha Touch 9  , a software development framework for mobile applications that can be deployed on iOS, Android, BlackBerry, Windows Phone systems. The application shares the same mapping and database infrastructure of the desktop version of the existing webGIS and therefore maintenance and operational efforts are minimized. It consists of (i) a main panel for map and data visualization (Figure  8 ) with the information (crop ET, rainfall and irrigation) aggregated to daily, 7-day and from the start of irrigation; ii) a data input panel (Figure  9 ), where users can log parcel-specific management activities such as irrigation volumes and crop type; and (iii) 7-day weather forecast, currently based on OpenWeatherMap 10  services.
Acknowledgments This research was carried our within the  "Earth Observation for water management project "  (EO4Water ) funded under  Die Österreichische Forschungsförderungsgesellschaft (FFG)  and the  Austrian Space Applications Programme (ASAP). We thank  Nikolaus Neugebauer  from  BOKU  for help in processing the Earth observation and agrometeorological data. We thank  Prof. Guido D'Urso  from the  University of Naples Federico II  for fruitful discussion and feedback on the application of the remote sensing methodologies. We also acknowledge feedback from colleagues involved in the operational development of satellitebased services:  C. De Michele ,  L. Marotta ,  S. Falanga Bolognesi  and  M. Colandrea  from  Ariespace. The smartphone application was developed by  Mateusz Żółtak. The framework for the cost analysis was initially developed in the context of the  SIRIUS-GMES  project and reviewed by  Anna Osann  from  University Castilla La Mancha ,  Andrew Groom  from  Geo-Intelligence Airbus Defence and Space and C.
In recent years, BioSAXS has followed the same path as MX in terms of increased automation of experiments with the implementation of sample-changer robots  (Round et al. , 2008; David & Perez, 2009; Pernot et al. , 2010)  and automated online data analysis  (Petoukhov et al. , 2007; Hura et al. , 2009; Nielsen et al. , 2009; Kieffer & Karkoulis, 2012). The modularity of ISPyB allows the data model to easily be extended to include other experimental techniques. BioSAXS is a natural extension, as its requirements for sample tracking and data handling are similar to those for MX and the respective user communities overlap. With the support of BioStruct-X funding in 2012, EMBL Hamburg, DLS and the ESRF set up a collaboration to extend ISPyB to include BioSAXS experiments. The first phase of the project (described in this article) was to develop a generic data model for all SAXS experiments and possible data-processing pipelines.
, 2015). The ability to handle many samples in an automated way increases throughput and minimizes errors during manipulation. However, preparing a large number of samples can lead to data-entry errors when describing the list of samples in the data-acquisition software. This potentially causes a significant reduction in efficiency and   A further extension of ISPyBB is planned with the implementation of automated sample preparation using liquid-handling robotics at high-throughput crystallization (HTX) facilities and parsing sample details from their internal databases such as CRIMS (CRystallization Information Management System; https://embl. fr/htxlab) to ISPyBB. Modern online analysis tools  (Petoukhov et al. , 2007; Incardona et al. , 2009; Kieffer & Wright, 2013 ) provide a wealth of information (reduced one-dimensional curves, models etc.
Although using standard HPLC columns typically represents lower throughput in terms of samples compared with standard acquisition, in some cases fast columns (e. g. Superdex 200 Increase 5/150 GL) can be used whose overall runtime is close to the duration of a complete standard acquisition dilution series. Data-acquisition parameters for SEC at BM29 are typically one frame per second (but five frames per second is achievable with the current setup) for the time the sample takes to elute (from 10 min to several hours depending on the type of SEC column and the flow rate). Online processing is required to provide real-time feedback in order to properly perform such experiments. Thus, screening all the acquired data and automated analysis results in a database that presents the relevant information and enables feedback on further experimental strategy.