dataesr/scientific-paragraphs-categorization huggingface dataset:
The calibration strategy For photometry, as with astrometry, Gaia is considered to be self-calibrating. The sky, or at least a large percentage of the objects observed, are non-variable down to a little above the accuracy levels achieved by Gaia, in other words, a few mmag at most. After calibration the observed fluxes of such assumed not-variable sources should be the same within the standard uncertainty margins, independent of the time of observation or the applicable calibration unit. The internal calibration therefore aims at creating an internal photometric system consistent over the entire instrument using all suitable sources. This system is achieved by solving the large-and small-scale calibrations in an iterative process, as these two kinds of calibrations cannot be obtained simultaneously, requiring data sets with very different time lengths.
Distribution over the sky The distribution of the number of observations and number of sources on the sky, as shown in Fig. 2  and 3 , expose some of the extremes of the conditions under which the photometric calibrations have to operate. The variation of the number of observations depends on the way Gaia scans the sky. The scan is necessarily built around the ecliptic plane, keeping the spin axis of the satellite at a fixed angle of 45 • from the direction of the Sun (see also Gaia  Collaboration et al. 2016b ). The precession of the spin axis around the direction of the Sun and the rotation of the satellite provide a complete sky coverage in at least two scan directions every 6 months. Gaps in the coverage can be seen as darker red bands, and are caused by various interruptions of the data stream, some of which will be recovered at a later stage in the reductions (see Gaia  Collaboration et al.
The completeness as a function of magnitude of the photometric data in this first Gaia data release is affected by a range of external factors, such as the priorities defined for downloading data from the satellite, and difficulties in the initial data treatment, particularly related to poor performances in periods of high data volume. In both cases data for faint stars was selectively lost (see for more details  Arenou et al. 2016). When the problem was caused by the on-ground processing, the data can, and will, be recovered in the next processing cycles. A total of about 1. 14 billion sources were processed successfully. Around 0. 1 per cent of the data (about 1. 2 million stars) is not included in this release becasuse it is either too red or to blue to fit within the calibration boundaries.
Conclusions This paper presents the photometric data included in the first Gaia data release. Only Gband photometry is included in this release. A high-level summary of the photometric data and processing is given here. The accompanying papers provide details as follows:  Carrasco et al. (2016)  for the detailed definition of the calibration models,  Riello et al. (2017)  for a description of the software solutions and processing strategies, and  Evans et al. (2017)  for an in-depth analysis of the results of the photometric processing. Not all instrumental effects are calibrated at this early stage in the mission and the photometry published in Gaia DR1 is the result of the first cyclic processing with no iteration between the various systems in the Gaia Data Processing and Analysis Consortium.
Some calibration effects have not yet been fully understood and there seem to be some systematics at the 10 mmag level particularly around G = 11 (see  Evans et al. 2017). The Gaia photometric catalogue covers the entire sky, providing measurements of the average brightness of sources down to magnitude 21 (at different levels of completeness at this stage) in a single photometric system. The overall accuracy of the photometric data reaches the 3 -4 mmag level. Future releases will enhance this photometric catalogue with improved G band photometry and the addition of colours and low-resolution spectra for all sources thus creating the most complete and accurate photometric catalogue to date.
Acknowledgements. This work was supported in part by the  MINECO (Spanish Ministry of Economy) -FEDER  through grant  ESP2013-48318-C2-1-R  and  MDM-2014-0369  of  ICCUB (Unidad de Excelencia 'María de Maeztu'). We also thank the  Agenzia Spaziale Italiana (ASI)  through grants  ARS/96/77 ,  ARS/98/92 ,  ARS/99/81, I/R/32/00, I/R/117/01 ,  COFIS-OF06-01 ,  ASI I/016/07/0 ,  ASI I/037/08/0 ,  ASI I/058/10/0 ,  ASI 2014-025-R. 0 , ASI 2014-025-R. 1. 2015 ,and the  Istituto Nazionale di AstroFisica (INAF). This work has been supported by the  UK Space Agency , the  UK Science and Technology Facilities Council. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7-SPACE-2013-1 ) under grant agreement no. 606740. The work was supported by the  Netherlands Research School for Astronomy (NOVA)  and the  Netherlands Organisation  for.
(c) Statistical analysis (i) Learning phase: To test for the effect of training on bee performance (number of correct choices), data from the learning phase of 80 choices were analysed with a generalized linear mixed-effects model (GLMM) with a binomial distribution using the 'glmer' package within the R environment for statistical analysis  [49]. We initially fitted a full model with trial number as a continuous predictor, and group as a categorical predictor with two levels (trained to larger or smaller), plus an interaction term between predictors with subject as a random factor to account for repeated choices of individual bees. As the interaction term was not statistically significant in experiment 2, it was excluded from the final model.
We also assessed essential processing steps to pinpoint critical flaws that many current pipelines have in common. An important positive finding is that most proposed algorithms are able to produce tractograms containing 90% of the ground truth bundles, recovering about one-third of their volumetric extent. At the same time, most algorithms produce large amounts of false-positive bundles, even though they are not part of the ground truth. Results do not improve when employing higher-quality data or even using the gold standard field of local tract orientations at high spatial resolution. The findings highlight that novel technological and conceptual developments are needed to address these limitations.
Discussion We assessed current state-of-the-art fiber tractography approaches using a ground truth data set of white matter tracts and connectivity that is representative of the challenges that may occur in human in vivo brain imaging. Advanced tractography strategies in combination with current diffusion modeling techniques successfully recovered most VBs, covering up to 77% of their volumetric extent. This result demonstrates the capability of current methods and teams to adequately handle numerous artifacts in DWI and overcome local crossing situations during tract reconstruction. However, tractography also produced thick and dense bundles of plausible looking streamlines in locations where such streamlines did not actually exist. When focusing on the 64 bundles that were systematically recovered by the majority of submissions, 64% of them were in fact absent from the ground truth.
Methods Generation of ground truth fiber bundles. The set of ground truth long-range fiber bundles was designed to cover the whole human brain and features many of the relevant spatial configurations, such as crossing, kissing, twisting and fanning fibers, thus representing the morphology of the major known in vivo fiber bundles. The process to obtain these bundles consisted of three steps. First, a whole-brain global tractography was performed on a high-quality in vivo diffusion-weighted image. Then, 25 major long-range bundles were manually extracted from the resulting tractogram. In the third step, these bundles were refined to obtain smooth and well-defined bundles. Each of these steps is detailed in the following paragraphs. We chose one of the diffusion-weighted data sets included in the Q3 data release of the HCP  39  , subject 100307, to perform whole-brain global fiber tractography  52, 68.
1 mm, and a particle weight of 0. 002. Furthermore, we repeated the tractography six times and combined the resulting whole-brain tractograms into one large data set consisting of over five million streamlines. The selected parameters provided for a very high sensitivity of the tractography method. The specificity of the resulting tractogram was of lesser concern since the tracts of interest were extracted manually in the second step. Bundle segmentation was performed by an expert radiologist using manually placed inclusion and exclusion regions of interest (ROI). We followed the concepts introduced in ref. 40  for the ROI placement and fiber extraction. Twenty-five   7  Ambiguous correspondences between diffusion directions and fiber geometry. The three illustrations at voxel, local, and global level are used as an example to illustrate the possible ambiguities contained in the diffusion imaging information that may lead to alternative tract reconstructions.
To obtain smooth tracts without prematurely ending fibers, we simulated a diffusion-weighted image from each original tract individually using Fiberfox (www. mitk. org33). Since no complex fiber configurations, such as crossings, were present in the individual tract images and no artifacts were simulated, it was possible to obtain very smooth and complete tracts from these images with a simple tensor-based streamline tractography. Supplementary Fig. 7  illustrates the result of this refining procedure on the left CST. Simulation of phantom images with brain-like geometry. The phantom diffusion-weighted images (Supplementary Movie 3) were simulated using Fiberfox (www. mitk. org33), which is available as open-source software. We employed a four-compartment model of brain tissue (intra and inter-axonal), GM, and cerebrospinal fluid (CSF)  33.
The following distances were used, with identical distances on both sides for lateralized bundles: 2 mm for CA and CP; 3 mm for CST and SCP; 5 mm for Cingulum; 6 mm for Fornix, ICP, OR, and UF; 7 mm for FPT, ILF, and POPT; 10 mm for CC, MCP, and SLF. The full script used to run this bundle recognition implementation was based on the DIPY library  78  (www. dipy. org) and is available online (Supplementary Software 1). Once VCs are identified, the remaining streamlines can be classified into ICs and non-connecting streamlines. The details of this procedure are described in Supplementary Note 4. We clustered the remaining invalid streamlines using a QuickBundles-based clustering algorithm  77. The best matching endpoint regions for each resulting cluster were identified by majority voting of the contained streamlines.
Following previously defined criteria of evaluation  79  , our study is mainly about accuracy with respect to the reference, rather than reproducibility or robustness of tractography. Statistical multi-variable analysis. Effects of the experimental settings were investigated in a multivariable linear mixed model. The experimental variables describing the methods used for the different submissions were included as fixed effects (Fig. 2b ). The VC ratio, the VB count, the IB count, the bundle overlap percentage, and the bundle overreach percentage were modeled as dependent variables, each of which is used for the calculation of a separate model. The submitting group was modeled as a random effect. The software SAS 9. 2, Proc Mixed, SAS Institute Inc. , Cary, NC, USA, was used for the analysis.
Acknowledgements K. H. M. -H. was supported by the  German Research Foundation (DFG) , grants  MA 6340/10-1 ,  MA 6340/12-1. M. D. was supported by the  NSERC   Discovery Grant  program as well as the institutional  Université de Sherbrooke Research Chair in Neuroinformatics. C. M. W. T. was supported by a grant (No. 612. 001. 104 )from the  Physical Sciences division of the Netherlands Organization for Scientific Research (NWO). The research of H. Y. M. , S. D. , S. S. , A. M. H. , and A. L. was supported by  VIDI  grant  639. 072. 411from   NWO. The research of F. G. was funded by the  Chinese Scholarship Council (CSC). M. Ch. was supported by the  Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program  ( CGS-D3 ) from the  Natural Sciences and Engineering Research Council of Canada (NSERC).
Results Sequencing results. After stringent filtering and quality control, 13,100,236 reads were obtained for: (1) the full-length COI barcoding region (658 bp) (amplicon COIF 6,659,598 reads) and (  2 ) a 235 bp fragment on the 5 0 region of the COI barcoding region (amplicon COIS 6,440,638 reads), from 32 samples comprising 16 eDNA and 16 invertebrate community DNA samples. Data for these two amplicons were obtained from a larger data set including additional amplicon libraries, sequenced on two lanes of MiSeq. Overall, the eDNA samples (extracted from filtered water samples) achieved good sequence coverage (mean number of reads per sample ( ± s. d. ): COIF: 269,769 ± 57,427; COIS: 259,723 ± 85,437; for exact number of reads per sample, see Supplementary Table  1 ). Some of the community DNA samples that contained only small amounts of pupal exuviae resulted in a lower number of reads for both amplicons.
01% and 0. 02%), a filtering level of 0. 01% was set for all ecological analyses. Removal of OTUs present at o0. 01% yielded equivalent levels of OTU genus richness for the community DNA (37 genera) and eDNA (43 genera) according to year 2014 Chironomidae records of Llyn Padarn (31 genera; Fig. 1 ). Furthermore, filtering of reads below 0. 01% was within the limits of a small number of non-target reads detected in the positive control samples. The genus richness comparisons employed COIS data to ensure comparability between eDNA and community DNA for the Chironomidae below. According to the analysis of OTU accumulation curves (Supplementary Figs  1  and 2 ) versus sequence coverage, a rarefaction depth of 57,869 reads was applied across all water samples (Supplementary Fig. 1a ). To subsample Animalia OTUs in our samples a rarefaction depth of 24,914 reads per sample was used (Supplementary Fig.
5%) Animalia OTUs, of which 212 consisted of Arthropoda (including 184 Insecta). Although not the focus of the study, metabarcoding of the eDNA samples (COIS used here as an example) also yielded matches to fish (Phoxinus phoxinus), amphibian and terrestrial OTUs represented at high read frequencies or distributed across numerous independent samples. Of the terrestrial taxa, spider OTUs from the Segestriidae (3,753 reads) and Thomisidae (1,858 reads) families, a millipede OTU (7,312 reads), orthopteran OTU (14,237 reads) and 2,114 reads from domesticated cow (Bos taurus) were recovered from multiple samples throughout the year, in addition to a broader diversity of terrestrial groups represented at lower frequencies in the dataset. Temporal trends of OTU richness from eDNA samples. Measures of OTU richness were calculated exclusively for eDNA samples and plotted against time to detect possible seasonal variations (Supplementary Fig.
5 ). All samples were rarefied at an equal depth appropriate for each amplicon (total diversity dataset: 57,869 reads per sample, animal diversity dataset: 24,914 reads per sample, for all water samples). Mean Animalia richness for COIS (±s. d. ) was 37. 8 (±10. 4), and for COIF, 31. 4 ( ± 11. 4) (Supplementary Fig. 5a ). A significant correlation was detected (Spearman's correlation, Po0. 05) between the OTU Animalia richness estimates derived from COIF with time and temperature, but not with pH or dissolved oxygen (D. O. ). In addition, mean total richness for COIS ( ± s. d. ) was 73. 1 ( ± 21. 2), and for COIF, 88. 1 ( ± 26. 9; Supplementary Fig. 5b ). A significant correlation was detected (Spearman's correlation, Po0. 05) between the COIF (total richness), time, temperature and D. O. , but not pH.
No significant correlation was found for COIS for the Animalia and total richness and any of the above parameters. Community structure (b-diversity) from eDNA samples. We used eDNA samples to look into possible changes in community structure over time, for the Animalia identified diversity as well as the total diversity in the dataset. For the eDNA samples, nMDS analysis (Sørensen index) of total diversity for both amplicons (Fig. 2 ), delimited patterns of seasonal variations driving community composition with qualitatively higher temporal resolution recovered from the smaller amplicon COIS. ANOSIM analyses also supported two main groupings, 'winter' (November to April) and 'summer' samples (April-Oct) (COIF: ANOSIM sig. level ¼ 0. 1%, Global R ¼ 0. 717, COIS: ANOSIM sig. level ¼ 0. 2%, Global R ¼ 0.
2 ). For the Chironomidae assigned OTUs, COIS identified 103 OTUs from eDNA and 94 OTUs from community DNA samples (138 unique OTUs in total). Using a combination of BLAST ID Z99% and the online Barcode of Life Database (BOLD) species assignment tool  36  , 73 OTUs (53% out of 138 unique) were assigned species level taxonomic information. Analysis of historical species occurrence data collected by the Environment Agency (EA; summer surveys 2003-2013) in Llyn Padarn (N. Wales, UK) indicated the presence of Z99 Chironomidae species from 57 genera. Moreover, Fig. 1  illustrates the qualitative overlap between the number of chironomid genera delimited by the current community DNA (65%), eDNA (61%) and taxonomy approaches. Similarly, see Supplementary Fig. 8  for overlap between each method for the four summer time points used for analysis.
To allow across method comparisons we compared OTU abundances for Chironomidae OTUs occurring in both eDNA and community DNA datasets (45 OTUs). Abundances differed significantly among different OTUs (GAM, F ¼ 4. 688, P valueo0. 001) with a significant effect of the temporal smoothing term (GAM, F ¼ 2. 561, P ¼ 0. 047; Table  1 ). In addition, abundances did not differ significantly between methods (GAM, F ¼ 0. 013, P value ¼ 0. 908), but a significant OTU identity Â method interaction (GAM, F ¼ 1. 733, P value ¼ 0. 003) was found. The abundance of OTU reads was also found to be significantly positively correlated with expected species frequency (ranging from 0. 01 to 0. 79) across 97 sites in the United Kingdom (UK) (two-way analysis of variance (ANOVA), R 2 ¼ 0. 087, P value ¼ 0. 003; Table  1 ), using previously catalogued Chironomidae species frequency data  37  (Fig.
Both metabarcoding amplicons detected large amounts of Animal phylum level diversity from eDNA samples, showing broad representation across the freshwater taxonomic biosphere, including the Arthropoda (Supplementary Fig. 4 ). Within the Arthropoda, the dominance of Insecta, Maxillopoda and Malacostraca (Crustacea) also demonstrates the utility of eDNA metabarcoding for characterization of freshwater ecosystemwide biodiversity. There is increasing exposure of the use of eDNA metabarcoding for the detection of fish and amphibians  19, 20  , as also recorded here. A more novel concept is the ability of freshwater systems to integrate eDNA biodiversity information from terrestrial sources. Terrestrial species found in our dataset, such as spider, millipede and orthopteran species, or the ubiquitous Bos taurus (please also note, that no bovine serum products were used in the HTS library preparations), are all commonplace in the surrounding area of the study site and were detected by the analysis of eDNA residing in the lake water samples.
The ability of freshwater catchments to contain eDNA from broader habitat biodiversity therefore presents an opportunity for further research regarding the relationship between aqueous eDNA and biodiversity at the landscape scale. Focusing on the Chironomidae richness estimates derived from the analysis of the short COI fragment (Fig. 3 ), we can see that the COIS amplicon yielded 138 unique OTUs from both sample types throughout the year. The analysis of the COIS amplicon therefore provided valuable comparative qualitative and quantitative data both within the metabarcoding datasets and between the historically collected data for Llyn Padarn and the rest of the UK  37. Other eDNA studies have focused mainly on macroorganisms such as fish or amphibians whereby skin cells and mucus are a likely primary source of eDNA  8.
2 ). Compositional turnover is thereby expected to result from seasonal variation in species abundances, increasing sources of contemporary eDNA, and environmental degradation decreasing levels of past eDNA accumulation. Using GAM modelling facilitated comparison between read abundances of individual OTUs derived from eDNA and community DNA analyses. Numbers of read abundances differed between OTUs over time and between eDNA and community DNA abundances at the individual OTU level (Table  1 ). There was also a significant positive association between the abundance of sequencing reads derived from the present study and species frequency at the national scale (Fig. 4 ). Therefore, lower abundance OTUs from the present study occur at lower frequencies and more abundant OTUs are more common, according to an extensive database of Chironomidae occurrence across the UK  37  (Fig.
Sampling was conducted at approximately 3-week intervals for 1 year (16 time points), using standardized sampling methodology, and collecting simultaneously water and Chironomidae samples. The two sites were sampled always in the same sequence (S1, then S2) between 08:30-11:30 hours, including consecutive collection of water samples, invertebrate samples, followed by water metadata (pH, Dissolved Oxygen (D. O. ), conductivity and water temperature), using a calibrated YSI Pro Plus multi-meter. As only water and exuviae (shed skins) were collected and the work was performed in collaboration with the EA and NRW, a permit was not required. Chironomid exuviae collection and eDNA filtration. Invertebrate samples in the form of chironomid exuviae (shed pupal skins) were collected using the field collection protocols for the Chironomid Pupal Exuviae Technique (CPET)  52  , with a 250 mm mesh collection net (Supplementary Methods).
For the COIF amplicon, the whole barcoding region was amplified and sequenced, but because of the current limitations of MiSeq sequencing read lengths, only the forward reads (R1) were used for analysis. Consequently, the per base quality drop expected in Illumina MiSeq data at the tail of the forward reads was inspected in FastQC and all reads were truncated at 250 bp and then quality filtered as above. Next, chimeras were removed (uchime_denovo) using a de novo delimitation approach. An operational taxonomic unit (OTU) table was created using OTU clustering at 97% similarity (USEARCH). Clustering at 97% similarity level was chosen based on existing knowledge of intraspecific diversity for Chironomidae  39  , since previous studies suggest that chironomid intraspecific diversity ranges between 0 and 4.
2% (ref. 39) or 0 and 4. 9% (ref. 62). Taxonomy was then assigned to the OTU table using BLAST þ (megablast) 63 against a reference COI database. The reference library was compiled from NCBI GenBank, by downloading all COI sequences, 4100 bp, excluding environmental sequences (20 June 2015, N ¼ 807,388 sequences) and higher taxonomic level information was edited using the GALAXY online software platform  64. Taxonomic assignment of the OTU tables and subsequent analysis was performed in QIIME  65. All analyses involving USEARCH, QIIME and BLAST þ were performed using the High Performance Computing Wales systems. Given the potentially sensitive nature of eDNA metabarcoding, low frequency sequences can either represent less abundant taxa, or possible false positives and low-level contaminant OTUs 66.