dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , r 2 = 0. 7 for data within 40 • S-40 • N), which is consistent with previous comparisons of ocean color and field estimates  (Siegel et al. , 2005). Furthermore, the median value (0. 23 mg m -3 ) and range in TCHLA (0. 001-92. 41 mg m -3 ) from MARE-DAT compare well with the median (0. 20 mg m -3 ) and range (0. 01-99. 24 mg m -3 ) in chlorophyll from the SeaWiFS climatology. It must be noted that the SeaWiFS algorithms were likely calibrated with some of the data subsets listed herein. Nonetheless, the substantial number of data considered in this exercise supports the conclusion that there is no significant representation bias of chlorophyll a within MAREDAT. The pigment database covers the global spatiotemporal dy-namics of phytoplankton biomass in the global ocean reasonably well.
Distribution of accessory pigments The number of samples within the MAREDAT pigment database that contribute to the distribution of each of the accessory pigments is not equal (Fig. 7 ), as we did not discriminate against contributions without a full suite of accessory pigments. The concentration of chlorophyll a (Fig. 7a ) and the key accessory pigments (Fig. 7b-l ) generally exhibit a log-normal distribution about their mean. The DV Chl a distribution has the fewest observations (Fig. 7f ), potentially because this pigment is not routinely measured during HPLC analysis on high latitude samples. Figure  8  displays surface concentrations (as the average over the upper 20 m) of the main taxonomic pigments from the entire MAREDAT dataset. Figure  9  displays the zonal annual mean depth distributions for the upper 250 m, also using the entire dataset for averaging.
, 2001). For example, DV Chl a, which is indicative of the presence of Prochlorococcus spp. (Vidussi et al. , 2001) , shows highest concentration in  the tropical regions of the eastern Pacific and Indian oceans (Figs. 8f  and 9f ). This finding is consistent with prior reports on Prochlorococcus spp. distributions based on cell counts  (Goericke et al. , 2000; Buitenhuis et al. , 2012a). Similarly, fucoxanthin (Fuco) is widely considered to be diagnostic of diatoms in regions where diatoms dominate the autotrophic abundance (e. g. , high latitude productive areas;  Wright et al. , 2010) , and accordingly the MAREDAT data show a bias for Fuco toward polar regions (Figs. 8g and 9g, respectively). Finally, the MAREDAT pigment dataset shows that Zeax is relatively confined to the warm tropical and subtropical wa-ters of each basin (Figs.
Recommendations for use The quality-controlled MAREDAT pigment database may be exploited using any number of approaches to estimate phytoplankton community structure in the global ocean. Some of these approaches include the diagnostic pigment method to determine size fractions  (Vidussi et al. , 2001; Uitz et al. , 2006) , least-squares solutions  (Letelier et al. , 1993; Andersen et al. , 1996) , CHEMTAX analysis  (Mackey et al. , 1996)  and similar approaches  (Goericke and Montoya, 1998; van den Meersche et al. , 2008). Regardless of method, care must always be taken to ensure that the regional or global algorithms applied are appropriate for the temporal or spatial scale in question. Furthermore, results should be validated with independent measures of assemblage composition (e. g. , flow cell counts, microscopy) when possible.
New global MAREDAT compilations of phytoplankton species abundance and biomass data from microscopic and cell counting methods (see this special issue) will aid in mapping discrete populations sampled by the pigment database for an effective global chemotaxonomic analysis. The presence/absence species information from MAREDAT, for example, may better inform accurate selection of "seed" pigment ratios that are needed for achieving robust quantitative results from CHEM-TAX  (Mackey et al. , 1996; Wright et al. , 2010). In contrast, abundance-based biomass data from MAREDAT may allow for quantitative comparison with the CHEMTAX results (e. g. ,  Llewellyn et al. , 2005). Future assessments will benefit greatly from updated summaries on plankton pigment ratios  (Higgins et al. , 2011). Ideal applications for chemotaxonomic workup results of the MAREDAT data are to (a) better ground-truth satellite estimates of phytoplankton functional types or size fractions, (b) determine biological niches in the ocean from phytoplankton community structure, and (c) evaluate MAREDATbased results against output from ecosystem models that translate physiological observations into phytoplankton community distributions.
While climatological mean Chl a signals from both MAREDAT and SeaWiFS are in good agreement, chlorophyll and accessory pigment concentrations in a given region vary widely in time, especially in areas with large seasonality in phytoplankton composition  (Yoder et al. , 1993). Thus, if results from MAREDAT pigment analysis are to be used for model comparisons, a point-to-point evaluation by month is recommended. The MAREDAT pigment database is viewed as a living dataset, with the expectation of new HPLC data contributions and improvements in the future. We anticipate that new product-specific datasets, such as those forming the MARE-DAT initiative, will permit exciting synergies and scientific gains across several disciplines.
A1 Data table A full data table containing all pigment suites can be downloaded from the data archive PANGAEA: http://doi. pangaea. de/10. 1594/PANGAEA. 793246. The data file contains longitude, latitude, depth, sampling date and time, total chlorophyll a and total accessory pigment concentrations, the individual pigment concentrations, all quality flags, as well as the full data references. As a subset, a distinct archive reference is dedicated to the dataset originating from the LOV: http://doi. pangaea. de/10. 1594/PANGAEA. 808535.
Acknowledgements. J. Peloquin, C. Swan, N. Gruber and M. Vogt were funded by ETH Zürich. C. O'Brien has received funding from the European Community's Seventh Framework  Programme (FP7 2007 -2013)  under grant agreement no. 238366. We are grateful to two anonymous reviewers for their evaluation of this manuscript. We thank Erik Buitenhuis for producing the gridded netCDF product, Stephane Pesant for archiving the data on www. Pangaea. de, and Dave Carlson and Hans Pfeiffenberger at ESSD for their patience. We would like to further acknowledge Gerald Moore, Denise Cummings, Tony Knap, Dave Robins, Dave Suggett and Guy Westbrook for their HPLC data contributions. We are equally grateful for all the project PIs who contributed data, as well as for the anonymous staff who took part in the sampling work during the cruises, who carried out the HPLC analysis, and without whom the construction of such a database would not have been possible.
1 8 Kozlowski et al. (2011) 68 Vernet, M. Palmer Station LTER lmg9901 355 3. 0 8 Kozlowski et al. (2011) 69 Prezelin, B. Palmer Station LTER pd9401 408 5. 4 8 Kozlowski et al. (2011) 70 Vernet, M. Palmer Station LTER pd9701 588 1. 3 8 Kozlowski et al. (2011) 71 Prezelin, B. Palmer Station LTER pd9109 302 9. 2 8 72 Prezelin, B. Palmer Station LTER pd9301 651 0. 9 8 73 Prezelin, B. Palmer Station LTER pd9307 319 0 8 74 Vernet, M. Palmer Station LTER pd9501 401 5. 8 8 Kozlowski et al. (2011) 75 Vernet, M. Palmer Station LTER pd9601 528 0. 2 8 Kozlowski et al. (2011) 76 Marty, J. -C. ANTARES Cruise 3 43 0 9 Marty (2004), doi:10. 1594/PANGAEA. 136858 77 Wasmund, N. BASYS BASYS I (1996) 17 19. 2 9 Wasmund (2006), doi:10. 1594/PANGAEA. 399756, 399758-61 78 Head, E. BAF 89-3 28 0 9 Head (2002), doi:10.
1594/PANGAEA. 808261 79 Barlow, R. NERC BOFS North Atlantic 59 1. 6 9 Lowry; BODC (2004), doi:10. 1594/PANGAEA. 198134, 198151, 198417, 198419 80 Nelson, N. AMMA amma-rb-06 9 0 10 81 Mannino, A. BIOME B01 45 0 10 82 Mannino, A. BIOME B02 144 0 10 83 Mannino, A. BIOME B03 77 0 10 84 Mannino, A. B03 Mission Bay 6 0 10 85 Mannino, A. BIOME B04 126 0 10 86 Muller-Karger, F. CARIACO car124 8 0 10 87 Muller-Karger, F. CARIACO car125 8 0 10 88 Muller-Karger, F. CARIACO car126 5 0 10 89 Muller-Karger, F. CARIACO car127 4 0 10 90 Muller-Karger, F. CARIACO car128 8 0 10 91 Muller-Karger, F. CARIACO car129 8 0 10 92 Muller-Karger, F. CARIACO car130 8 0 10 93 Mitchell, B. G. cce-p0605 44 0 10 94 Hooker, S. en431 49 0 10 95 Behrenfeld, M. gp1-06-ka 31 3. 1 10 96 Subramaniam, A. jun06atl 83 1. 2 10 97 Dierssen, H.
(2009) 131 Gibb, S. AMT Program Cruise 4 268 5. 2 7 Gibb et al. (2000) 132 Gibb, S. AMT Program Cruise 5 230 0. 4 7 Gibb et al. (2000) 133 Barlow, R. AMT Program Cruise 6 198 0. 4 7 Aiken et al. (2009) 134 Moore, G. AMT Program Cruise 6B 27 10. 2 7 Aiken et al. (2009) 135 Suggett, D. AMT Program Cruise 7 243 0. 3 7 Aiken et al. (2009) 136 Holligan, P. AMT Program Cruise 8 363 0. 4 7 Aiken et al. (2009) Data sources: 1 http://www. obs-vlfr. fr/proof/vt/op/ec/biosope/bio. htm, 2 Personal communication, 3 http://globec. whoi. edu/jg/dir/globec/nep/ccs/process/, 4 http://usjgofs. whoi. edu/jg/dir/jgofs/, 5 http://hahana. soest. hawaii. edu/hot/, 6 tp://bats. bios. edu/batsformbottle. html, 7http://www. bodc. ac. uk/, 8 http://pal. lternet. edu/data/, 9ttp://www. pangaea. de/, 10ttp://seabass. gsfc.
MODBASE (http://salilab. org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence±structure alignment, model building and model assessment (http:/salilab. org/modeller). MODBASE uses the MySQL relational database management system for ¯exible querying and CHIMERA for viewing the sequences and structures (http://www. cgl. ucsf. edu/chimera/). MODBASE is updated regularly to re¯ect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets.
The largest data set contains 1 262 629 models for domains in 659 495 out of 1 182 126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with signi®cant similarity scores and models assessed to have the correct fold despite insigni®cant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e. g. the 53 new structures produced by the consortium allowed us to characterize structurally 24 113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.
INTRODUCTION Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms, including humans. We are now faced with assigning, understanding and modifying the functions of proteins encoded by these genomes. This task is generally facilitated by protein 3D structures  (1) , which are best determined by experimental methods such as X-ray crystallography and NMR spectroscopy. Over the past 2 years, the number of sequences in the comprehensive public sequence databases, such as Swiss-Prot/ TrEMBL  (2)  and GenPept  (3) , have increased by a factor of 2. 3 from 522 959 to 1 208 659 on  August 15, 2003. In contrast, despite structural genomics, the number of experimentally determined structures deposited in the Protein Data Bank (PDB) increased by a factor of only 1.
The accuracy of automated comparative protein structure modeling has been quanti®ed by the CAFASP effort (8) as well as automated web servers EVA  (9)  and LIVEBENCH  (10). The process of comparative protein structure modeling usually requires the use of a number of programs to identify template structures, to generate sequence±structure alignments, to build the models and to evaluate them. In addition, various sequence and structure databases that are accessed by these programs are needed. Once an initial model is calculated, it is generally re®ned and ultimately analyzed in the context of many other related proteins and their functional annotations. To facilitate these tasks for both expert and novice users, we have developed several programs, servers and databases (Fig. 1 ). In this paper, we highlight the improvements of MODBASE that were implemented since the previous reports  (7, 19, 20).
MODBASE core Models in MODBASE are calculated using MODPIPE, our entirely automated software pipeline for comparative modeling  (16). MODPIPE can calculate comparative models for a large number of protein sequences, using many different template structures and sequence±structure alignments. MODPIPE relies on the various modules of MODELLER for its functionality and is streamlined for large-scale operation on a cluster of PCs using scripts written in PERL. The templates used for model building consist of representative multiple structure alignments extracted from DBALI  (11). These alignments were prepared by the SALIGN module of MODELLER (M. S. Madhusudan, M. A. Marti-Renom, A. Sali, in preparation), which implements a multiple structure alignment method similar to that in the program COMPARER  (21).
Sequence pro®les are constructed for both the target sequences and the templates by scanning against the Swiss-Prot/TrEMBL database of sequences, relying on the BUILD_ PROFILE module of MODELLER (N. Eswar, M. S. Madhusudhan and A. Sali, in preparation). BUILD_ PROFILE is similar to PSI-BLAST  (22) , except that local dynamic programming is used instead of the BLAST heuristics. Sequence±structure matches are established by aligning the target sequence pro®le against the template pro®les, using local dynamic programming in the SALIGN module and an assessment of statistical signi®cance similar to that of PSI-BLAST  (22)  and COMPASS  (23). Signi®cant alignments covering distinct regions of the target sequence are chosen for modeling. Models are calculated for each of the sequence± structure matches using MODELLER  (24).
The models in the version of MODBASE available until the end of 2003, however, were calculated using an earlier version of MODPIPE. These models were based on single template structures and built using sequence±structure matches generated by PSI-BLAST  (22)  and IMPALA  (26). Models in MODBASE are organized into data sets. The largest data set contains models of all sequences in the Swiss-Prot/TrEMBL database that are detectably related to at least one known structure in the PDB. Currently, there are 1 262 629 models for domains in 659 495 of the 1 182 126 sequences in the Swiss-Prot/TrEMBL database, with an average length of 235 residues per model. For example, there are models for 32 985 human sequences, 22 880 sequences from Arabidopsis thaliana, 15 195 sequences from Drosophila melanogaster and 9691 sequences from Escherichia coli.
ACCESS AND INTERFACE MODBASE is queryable through the web at http://salilab. org/ modbase by PDB codes, Swiss-Prot/TrEMBL and GenPept accession numbers, annotation keywords, model reliability, model size, target±template sequence identity, alignment signi®cance, and sequence similarity to the modeled sequences as detected by BLAST. Models in MODBASE are organized into a number of data sets whose access by different users is regulated using a cookie mechanism (http://www. acm. org/crossroads/xrds7-1/ cookies). The largest data set includes all modeled sequences from the Swiss-Prot/TrEMBL database and is freely accessible to all academic scientists. Other data sets include models calculated for NYSGXRC, MODWEB data sets from anonymous users and data sets associated with our other modeling projects.
The output of a search is displayed on pages with varying amounts of information about the modeled sequences, template structures, alignments and functional annotations. These tables also contain links to other sequence, structure and function annotation databases, such as PDB (4), GenBank (3), Swiss-Prot/TrEMBL (2), CATH  (32) , Pfam  (33) , ProDom  (34) , and UCSC Genome Browser  (35). In addition, MODBASE models are directly accessible from the Swiss-Prot/TrEMBL sequence pages at http://www. expasy. org and UCSC Genome Browser at http://genome. ucsc. edu.
Visualization of sequences, structures and alignments with CHIMERA To simplify the process of visualizing the models contained in MODBASE, we created an extension to the CHIMERA Molecular Modeling System, which was developed by the researchers in the Resource for Biocomputing, Visualization, and Informatics at UCSF (Fig. 2 ) (http://www. cgl. ucsf. edu/ chimera)  (30). The data contained in a MODBASE entry are divided among three different ®les: a template ®le, a model ®le and an alignment ®le. Manually downloading and opening these ®les with visualization tools can be a cumbersome process. The new CHIMERA extension enables a web browser to communicate directly with CHIMERA. By clicking on a single link associated with each MODBASE model, information related to the model is transmitted to CHIMERA, which then displays the structures of the template and the model; their alignment is also displayed using CHIMERA's multiple sequence alignment viewer, MultAlign Viewer.
FUTURE DIRECTIONS MODBASE will be updated at least monthly to re¯ect the growth of the sequence and structure databases, as well as improvements in the methods and software used for calculating the models. We also plan to integrate access to the sequence pro®les in the web-based interface and to include tools for target selection for structural genomics. Furthermore, we plan to improve the ¯exibility of searching for ligand binding sites. And ®nally, we will include additional search options to support associating structure and function.
Summary of the automated modeling by MODPIPE for seven of the 53 structures determined by NYSGXRCThe complete table is accessible at http://salilab. org/modbase/models_nysgxrc. html. ThePDB code', `Database accession number' and `Annotation' columns de®ne the template structure. `Total sequences' is the number of sequences in SwissProt/TrEMBL could be modeled reliably using the NYSGXRC structure as a template. A sequence is modeled reliably if it has a reliable PSI-BLAST E-value of `10 ±4 (`Fold'), a reliable model with model score b0. 7 (`Model') or both (`Fold and model').
ACKNOWLEDGEMENTS We are especially grateful to  Roberto Sa Ânchez  for constructing the ®rst version of MODBASE. We also thank  Valentin Ilyin ,  Bino John ,  William Lane ,  Maria Sammut  and  Edward Wittenstein  for their contributions to MODBASE, and  Tom Goddard  for his contribution to CHIMERA. We thank  Elaine Meng  for her assistance with preparing Figure 2. The project has been supported by  NIH/NIGMS   R01 GM 54762 ,  NIH/ NIGMS   P50 GM62529 ,  NIH/NCI   R33 CA84699 ,  NIH/NCRR   P41 RR01081  (TF),  Sun Academic Equipment Grant   EDUD-7824-020257-US , an  IBM   SUR grant  and an  Intel  computer hardware gift.
The zebrafish larva stands out as an emergent model organism for translational studies involving gene or drug screening thanks to its size, genetics, and permeability. At the larval stage, locomotion occurs in short episodes punctuated by periods of rest. Although phenotyping behavior is a key component of large-scale screens, it has not yet been automated in this model system. We developed ZebraZoom, a program to automatically track larvae and identify maneuvers for many animals performing discrete movements. Our program detects each episodic movement and extracts large-scale statistics on motor patterns to produce a quantification of the locomotor repertoire. We used ZebraZoom to identify motor defects induced by a glycinergic receptor antagonist. The analysis of the blind mutant atoh7 revealed small locomotor defects associated with the mutation.
To identify core and tail positions for large datasets, videos were obtained on multiple larvae simultaneously over long periods of time and at high resolution using a highspeed camera run in a streaming-to-disk interface (Methods). Typically 500-1000 movements from seven larvae were recorded per dish in four minutes and eight dishes were monitored in parallel. To simplify tracking, we placed larvae in conditions that reduced overlapping in the z-plane during swimming (Methods; overlaps occurred on average once every 145 s per larva). We developed an offline 2D tracking method for identifying and separating each animal even when in close contact (Methods, Figure  1 ). For each larva several features were identified, a core position that included the head and swim bladder (Figure  1A ) and ten points along the tail (Methods and Figure  1B ; Video S1).
We observed that ZebraZoom agreed with the trained experimenter 82. 5% of the time for the recognition dataset (85% for S, 82% for T, and 79% for E; Figures  4B, C ; Table  1  and Methods). When compared to four independent experimenters, ZebraZoom reached 91% accuracy for categorization of stereotypical maneuvers that all experimenters had unanimously identified and 76. 4% on average for all maneuvers (73. 2-82. 5%, Table  1 ). Once validated, we applied the ZebraZoom categorization algorithm on a large dataset of 44,688 movements of WT larvae (Figure  4D ). We identified 14. 911 S (33. 36%), 21,432 T (49. 96%), and 8,345 E (18. 67%). The distribution of global parameters for the three classes of maneuvers were similar in terms of number of oscillations and duration, but they differed in terms of mean TBF, heading direction range, distance traveled and speed (Figure  4D ).
ZEBRAZOOM TRACKING ALGORITHM The first step is to track the core and then the tail for all larvae over time. Written in C++ using the openCV library, the program identified the center position and heading direction of each larva (Figures  1Ai-vi ). The algorithm used a Hough transform to identify the eight wells. For each well the background was estimated as the maximum pixel value over all frames of the video recording (Figure  1Aii ) and then subtracted for all frames for that well (Figure  1Aiii ). The resulting image was converted to binary (Figure  1Aiv ). An erosion filter was applied twice in a row with a 3 by 3 structuring element (Figure  1Av ). The "core" of the larva referred to the resulting connected components that had an appropriate area (between 0. 0871 and 0. 8712 mm 2 ). The core of the larva included the head and the trunk with swim bladder (Figure  1Avi ).
DETECTION OF MOVEMENTS Algorithms for the detection of movements and the behavior analysis were written in MATLAB (The Mathworks, Inc. , USA). The detection of movement was based solely on thresholding the tail-bending angle measured over time (Figures  1C, D ). ZebraZoom detected the start of a movement when the value for the tail-bending angle at a given frame varied over 1. 15 • from the mean value of the tail-bending angle for the ten surrounding frames, or 29. 7 ms. To avoid separating single maneuvers into multiple events, movements that occur within 14. 8 ms of each other were merged. To avoid false positives we considered only movements in which the larva core had moved more than 0. 099 mm and where the range of tail-bending angle values was above 2. 86 degrees. Additionally, only events during which the eroded binary image of the larva had moved more than a set number of pixels between subsequent images were considered based on the parameters used for the erosion.