dataesr/scientific-paragraphs-categorization huggingface dataset:
The Yieldn index was the annual ESU n biomass estimate in tons of dry matter per hectare that was compared with estimates obtained from the MR product. (3) Each year, the ESU biomass production was computed from February 1 to the last harvest in the ESU (information collected during field campaign). FPIMR was obtained with MODIS fCover grassland time series (Equation (1)) and Yieldn was computed with Equation (3) using field measurements. In the case of the FPIMR, the proportion of non-productive vegetation (NPV) is not subtracted. Next, the biomass production variation index (ΔFPIMR or ΔYield depending on the data source) corresponding to the ratio of biomass produced for a given year divided by a baseline value (Equations (  4 ) and (  5 )) was computed (Figure  4C ). Because 2013 and 2014 are similar weather years and significantly different from 2012, the average of 2013 and 2014 biomass production is defined as the reference (or baseline) and 2012 is the year for which the variation of biomass production was analyzed.
However, this level of error was computed from a limited number of data points spanning a narrow geographic extent and is likely not representative of the national situation. Finally, it is important to consider that accumulated errors from both the MR image processing chain and ground data collection are reflected in the RMSE and RMSEPred. It is also important to note that sampling plots were not randomly selected because of the likelihood that sampled plots the same ESU belong to the same producer. It is difficult to involve many different landowners from the same place. Second, it was not always possible to weigh all harvested bales for yield estimates in a specific plot. In this case, an alternative solution was adopted that used average bale weight for those from similar plots and comprised of the same grass species.
Method In 2013, 26 HR images from four different sensors acquired between 16 February and 23 October were analyzed (Table  1 ). Almost half were acquired from the SPOT4 Take5 program  [58]. In 2014, 22 HR images from three sensors (Landsat7, Landsat8 and Deimos) acquired between 12 February and 20 November were processed. All images were supplied in orthorectified form, reprojected to Extended Lambert-2 and resampled to a resolution of 20 m using bilinear interpolation. For all images, fCover was obtained from the Airbus D&S Overland software as described in Section 2. A reference of local mean reflectance values for soils was obtained over the whole study site by using a zone of bare soil from one HR image. Each fCover pixel with a satisfying "Fit" value was conserved for further analysis.
Only HR fCover pixels with a good quality ("Fit" parameter) and belonging to grassland plots within the ESUs were selected. The grassland data source used was the 2012 register of declaration made by the farmers in the Common Agricultural Policy. We consider as grassland all plots declared as natural or temporary grassland, moors and heathland. For grassland plots containing less than 40% of bad quality HR pixels, the plot HR fCover is obtained by computing the weighted-average of the HR fCover pixels. The same process was applied within each ESU. This resulted in an HR fCover grassland time series for each ESU composed of a variable number of HR images with an irregular frequency of acquisition depending on the cloud cover and the location in the study site (spatial extent of the images).
The FPIHR was calculated using Equation (1) for the ESU that presented periods between two fCover values of 40 days or less  [23]. Daily HR fCover was obtained by applying a spline-based time interpolation function between each HR fCover data. As for the FPIMR in the direct comparison, the proportion of non-productive vegetation (NPV) is not subtracted. From the 178 potential ESUs, only 69 remained after accounting for persistent cloud cover during spring 2013. The agreement between FPIMR and FPIHR was examined. The indices were calculated for 2013 and 2014 over the same period (March 1 to October 23) according to the temporal coverage of the HR image time series used. The quality of the HR time series used to produce the reference data is discussed in relation with the results. The effect of the disaggregation step is addressed by comparing the characteristics of the linear regressions (slope, R 2 , and RMSE) obtained between the HR and MR indices derived from whole pixel surfaces without land cover distinction (Production Index: PIHR and PIMR) or taking into account only grassland surface (FPIHR and FPIMR).
Even with a potentially higher number of HR images in 2013 than in 2014 (26 vs. 22 images), the availability of HR images in 2013 was not consistent spatially and temporally. This is due to longer periods of cloud cover (greater than the 40 threshold used for interpolation of daily fCover between two dates) and a more variable spatial extent of HR images used. These results confirmed the importance of assessing the accuracy of the HR time series if it is used as reference data in MR product validation. The R 2 and the RMSE were consistently better with indices calculated from fCover obtained without land cover distinction than when derived from an fCover based only on grassland pixels. This could be caused by the disaggregation process. Although disaggregation was required when monitoring grasslands with MR data, it could have a negative impact on the quality of the FPIMR produced.
33) compared to the one obtained from direct comparison (R 2 = 0. 71). It was found with a sample of 28 ESUs and a minimum of 45% of grassland per ESU. A main reason is a small range of variability in biomass production observed between 2013 and 2014 (range of ΔFPIMR = 0. 15) compared to the one recorded between 2012 and 2013 (range of ΔFPIMR = 0. 59). Providing a conclusion based on this result is misleading. With the SPOT5 Take5 program conducted in 2015, the HR time series dataset could be enriched with an additional year (2013-2014-2015) providing a way to potentially get more interannual biomass production  [61].
Discussion A quality assessment of FPI derived from MODIS products and developed to support index-based forage production insurance in France was performed for three years (2012/2013/2014) over a limited geographic extent of two departments (Lot and Aveyron). Given the availability of MR vegetation index time series datasets within the scientific community, the number of index-based insurance products has increased. However, validation is often not performed. This work highlights why rigorous validation is critical before releasing a product to the user community. Validating MR satellite products directly with biomass production data remains difficult. Given the various source of uncertainty in the data, results show satisfactory FPI accuracy (i. e. , slope close to 1; R 2 between 0. 78 and 0.
93; RMSE less than 8%). Collecting sufficient production data with high quality biomass measurements to obtain a statistically meaningful dataset at the scale of a 6 km × 6 km ESU was very difficult. In this protocol, sampling rate was maximized by collecting biomass data from the highest number of plots possible (290 plots in 2012, 768 in 2013 and 557 in 2014). However, maximizing the sampling rate required use of biomass measurement methods that were less accurate than ones traditionally used at the plot level (e. g. , mowers, mechanical and electronic plate meters). Despite these procedural decisions, the sampling rate within original ESUs remains low (below 2%). Resized ESUs were defined (from 3600 ha to 1152 ha) using the spatial distribution of grassland plots belonging to a set of farmers.
Nevertheless, modifications to the validation guidelines were made based on the target variable (FPIMR derived from MODIS fCover) and the scale of application (France). Results showed that FPIMR could be used as a proxy to monitor annual biomass production of grasslands and its variations with a satisfactory levels of error. This suggests that FPI has reached validation stage level 1 according to CEOS-LVP criteria  [28]  and is now ready to be tested with the same methodology in other geographic contexts and compared with similar products (definition of validation stage level 2). Considering the future availability of continuous HR time series data from platforms such as Landsat-8 and Sentinel-2, the accessibility of HR time series provided by the SPOT4 Take5 program was a good opportunity to test their potential for an index-based insurance product.
On the other hand, other problems emerged. The consistency of FPIHR relies on HR times series availability (space-time continuous data available at a national scale), the capacity to process images from different sensors to produce a robust biophysical variable  [41] , and the management of large datasets, all of which represent needs and active research questions. In the case of the index-based insurance product, producing a ΔFPI with HR time series will not be possible before 2022, the time required to constitute a reference after the launch of both Sentinel-2 satellites in 2016. If needed, a possible transition could consist of using both HR and MR products by data fusion as it has been demonstrated that the precision of HR images and the repeatability of MR time series provide an optimal configuration for monitoring grassland production  [65].
Conclusions In the context of developing an index-based insurance product, this study presents a bottom-up approach for validating the use of an annual FPI as a surrogate for interannual biomass variation at a kilometric scale. Validating MR products remains challenging and constraining, especially given difficulties with ground data collection. Two protocols were designed to achieve a complete assessment of the FPI accuracy. Direct comparison, where FPIMR is compared with ground measurements of yield, led to good accuracy with R 2 = 0. 71 and RMSE = 14. 5%. The weakness of this analysis, the small number of ESU considered, is overcome by the indirect comparison approach. The HR time series, with images mainly from the SPOT4 Take 5 program and Landsat8 satellite, allow for an increase in the number of ESUs that can be processed (13 for the direct vs.
69 for indirect comparison). The relationships between HR and MR indices were quantified before disaggregation (PIHR and PIMR, R 2 = 0. 90 and 0. 93, respectively, for 2013 and 2014) and after (FPIHR and FPIMR, R 2 = 0. 78 and 0. 84, respectively, for 2013 and 2014). The percentage of grassland in the ESU was a key parameter for both variables. Whatever comparison is made, results showed that FPI can be improved by integrating meteorological variables to reduce remaining error  [66]. The assimilation of farmer practices like the method and number of harvests or fertilization should also improve the accuracy of the FPI  [67]. Moreover, given the pending release of Sentinel2 data, the feasibility of using HR sensors was discussed. This evolution in the processing chain is important and leads to mandatory modifications in data management.
The interest in using HR time series is due primarily to elimination of the disaggregation step in the computation of FPIMR  [68]. Quantitative results and operational simulation of the final product are nevertheless still required. Moreover, prior to adopting data from new HR platforms, it is important to keep in mind that several years are needed to constitute a reference from Sentinel-2 data. Beyond index-based insurance, FPI may find other potential applications by providing information about local grassland production in near-real time for farmers to better manage their resources or for public authorities to assess regional production anomalies  [69, 70].
Active Pixel Sensors used in High Energy Particle Physics require low power consumption to reduce the detector material budget, low integration time to reduce the possibilities of pile-up and fast readout to improve the detector data capability. To satisfy these requirements, a novel Address-Encoder and Reset-Decoder (AERD) asynchronous circuit for a fast readout of a pixel matrix has been developed. The AERD data-driven readout architecture operates the address encoding and reset decoding based on an arbitration tree, and allows us to readout only the hit pixels. Compared to the traditional readout structure of the rolling shutter scheme in Monolithic Active Pixel Sensors (MAPS), AERD can achieve a low readout time and a low power consumption especially for low hit occupancies. The readout is controlled at the chip periphery with a signal synchronous with the clock, allows a good digital and analogue signal separation in the matrix and a reduction of the power consumption.
The state-of-the-art of MAPS used for particle detectors is represented by the STAR pixel detector, which is equipped with the ULTIMATE chip  [4]. The ULTIMATE is an example of the traditional rolling shutter readout architecture. It achieves an integration time of 185:6 μs and a power density of 130 mW/cm 2   [5]  with a 928 by 960 pixel array (20:7 μm pitch). The integration time of the Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/nima rolling shutter depends on the number of rows for a given readout clock frequency. The frame readout time can be reduced by embedding row-wise parallel readout with increasing the number of column-level discriminators. However, without any kind of data compression inside the matrix, such a reduction in the frame readout time comes with increasing power consumption.
Data compression techniques inside the matrix have been studied to improve the readout speed. The main technique is the token-ring architecture with a datadriven and triggered readout. The speed of the token-ring technique is limited by the time that the logic needs to ripple through the whole daisy chain. However it can be improved by implementing a fast priority look-ahead logic, like in the ATLAS readout chip with a pixel array of 80 columns by 336 rows  [6]. This allows the readout speed to be independent on the column length by skipping the empty banks. The digital circuitry occupies 22% of the 50 μm by 250 μm pixel size with a power consumption of 4. 4 mW/double-columns  [7]. To gain more margin on material budget and data readout capability for the ALICE ITS upgrade, the ALPIDE (ALice PIxel DEtector) chip design aims for a power density and an integration time one order of magnitude below the specifications which cannot be achieved by the readout architectures mentioned above.
In Eqs. (8)(c), a factor 1 2 is used because the AERD readout circuit is arranged in double-columns. An example of the front-end gain is 60 μv=e À  [4] , 80 e À =μm means the particle generates 80 hole/electron pairs in 1 μm silicon thickness. 18 μm is the epi-layer thickness of the standard TowerJazz CIS process. The dynamic power consumption of the rolling shutter scheme included two parts: one is the hit data processing, the other one is the power consumption of the clock. P rscol ¼ P FErscol Â N col |fflfflfflfflfflfflfflfflffl ffl{zfflfflfflfflfflfflfflfflffl ffl} static power þ C L Â dv 2 Â N col Â N row Â Hit_density T clock Â N row |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} dynamic power for loading data þ C L Â N Â V 2 DD Â N col T clock Â N row |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl} dynamic power for clock distribution ðaÞ P rsin ¼ P FErsin Â N col |fflfflfflfflfflfflfflffl ffl{zfflfflfflfflfflfflfflffl ffl} static power þ C L Â V 2 DD Â N col Â N row Â Hit_density T clock Â N row |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} dynamic power for loading data þ C L Â N Â V 2 DD Â N col T clock Â N row |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl} 8 > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > < > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > : ð8Þ We use the chip dimension of the pALPIDEfs for a comparison in more detail.
For the rolling shutter with in-pixel comparator architecture, an additional zero-suppression circuit is needed in the  chip digital periphery. Because of the low power front-end and the zero-suppression technique of the AERD readout, the matrix power consumption of the ALPIDE is still smaller than the rolling shutter with in-pixel comparator scheme when the readout speed is faster than 7 MHz at a hit density level lower than 0. 1%. A power consumption below 24 mW is achievable when the hit density is extremely low (less than 0. 1%) at a high readout speed of 40 MHz. According to the simulation results with parasitic capacitances and resistances of the pALPIDEfs chip, the dynamic readout power consumption to transmit the data to the chip periphery is approximately 720 μW=hit. The energy used to read out one hit is around 72 pJ.
Conclusion As an alternative to the traditional rolling shutter readout architecture, the Address Encoder and Reset Decoder data-driven readout architecture has been implemented and tested, and shows significant advantages both in terms of readout time and power consumption. This allows the pALPIDEfs development to reduce integration time and power consumption well below the specifications for the ALICE ITS upgrade. The test results from the fabricated prototypes show that the AERD circuit operated at 10 MHz meets or exceeds specifications. Furthermore, this paper has shown that the readout speed can be boosted from 10 MHz to 40 MHz through the separation of the decode and reset phases without any extra silicon area.
a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (  5 ) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.
Motivation for the meeting Many databases exist that provide information to the scientific community to enable the understanding of particular classes of proteins. For example, the CAZy database  (1)  provides detailed information about carbohydrate enzymes, and the TCDB database provides the classification and descriptions of transporter proteins  (2). These databases are usually run by a world-leading expert, and most of these databases have a main focus on curating fundamental molecular data about proteins, often linking sequence, structural and functional features relevant to a broad range of fields including molecular biology, biomedicine and biotechnology. Because each resource has developed to serve a particular community of researchers, a variety of tools, techniques and philosophies have evolved to best serve their communities.
Participation Twenty-one principal investigators, each maintaining either a specialized protein bioinformatics database or a global protein resource at a large Bioinformatics centre attended this meeting. The participants at the meeting are pictured in Figure  1 , and the resources they represent are listed in the figure caption. Supplementary file S1 contains a short description of each of these resources, including those not described in the body of this report. Of course there were many relevant specialist protein resources that were not included due to limited space at the meeting. According to the Oxford University Press Online Molecular Biology Database Collection (3) there are 94 database resources that focus on one or a small number of protein families (http://www. oxfordjournals. org/our_journals/nar/database/subcat/3/10).
Session 2: Introduction to the protein resources An important goal of the meeting was to foster communication between specialist protein resources. We found that very few of the participants had met each other face-to-face despite the often close similarities in the work they perform. The second session gave the participants an opportunity to briefly introduce their protein resources. Twenty of the participants gave 5 min lightning talks using just two slides, a task that was challenging given the richness of their resources. Some participants had a greater challenge of introducing several databases in their talks such as Pantelis Bagos from the University of Thessaly who introduced gpDB and ExTopoDB as well as OMPdb  (5). However the participants rose to this challenge, and there was a real sense that common ground was established.
Session 3: Best practices The aim of this session was to identify best practices for maintaining and curating specialized resources. Four speakers were asked to present aspects of their curation, website or software tools that they thought could be adopted by others. Gert Vriend began the session by presenting his 10 rules for making a biological database. These rules had been developed through his experience in creating and running the GPCRDB  (6). They are aimed at offering guidance in making a successful, long term and sustainable resource. The presentation sparked a lively discussion, and as the participants agreed and endorsed these rules, they are presented in full in Gert's vernacular below: 1. Longevity: The one rule to rule them all. Gert asks that unless you can maintain your database for at least 10 years, then do not start.
David Landsman presented the Conserved Domain Database (CDD) at NCBI  (7). He described the importance of two aspects of their curation activities, first, that each of the alignments for CDD families were based on structural superpositions, manually edited to improve quality, and second, that CDD families can sometimes be split according to evolutionary history to increase the functional specificities of the families. Nozami Nagano presented EzCatDB, the Enzyme Reaction Database  (8). EzCatDB provides a hierarchical classification of enzyme reactions which takes particular care in curating the reaction intermediates. An Excel-based literature manager was presented which could be more widely used. Finally, Milton Saier presented the TCDB database. Over the past 20 years numerous tools have been developed with a focus on transporter proteins, including G-BLAST for annotating genomes and the SuperFamilyTree (SFT) programs which allow construction of phylogenetic trees showing protein, subfamily or family relationships based on BLAST bit scores  (9).
Session 4: Information flow The aim of this session was to discuss how to improve the flow of information both to and from the large data centres such as the National Center for Biotechnology Information (NCBI) and the EMBL-European Bioinformatics Institute (EMBL-EBI). Rob Finn's presentation was entitled, 'Challenges of integrating different resources into a single service and/or database'. He described the challenges faced by InterPro in integrating its 11 different protein family databases. The main message was that growth of the sequence databases puts pressure on the computational pipelines and consequently, there is continual pressure to move to faster search technologies and infrastructures. Gemma Holliday's talk on interoperability and communication between databases introduced the large array of existing enzyme databases (see Supplementary file S1).