dataesr/scientific-paragraphs-categorization huggingface dataset:
The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty.
Introduction Few scientists would dispute the principle that an estimate of uncertainty should be given with every measured value. However, meaningful adherence to this simple principle can be challenging, and in practice researchers commonly encounter datasets for which uncertainty information is generic, misleading, or absent. Climate data records (CDRs) are not immune to this problem, despite the fact that climatic signals are usually subtle (e. g. ,  Kennedy, 2014; Mahlstein et al. , 2012; Flannaghan et al. , 2014; Barnett et al. , 2005) , which adds to the importance of rigorous uncertainty characterization in CDRs (e. g. ,  Immler et al. , 2010). The question of how to derive and present uncertainty information in CDRs has received sustained attention within the European Space Agency (ESA) Climate Change Initiative  (CCI; Hollman et al.
Feedback gathered by CDR producers (e. g. ,  Rayner et al. , 2015)  shows that modellers find it too time consuming to develop a level of appreciation of observational datasets that allows them to make confident judgements about such matters. For this reason, CDRs need to include validated uncertainty information that modellers trust for contextualizing model-data discrepancies. Until this is achieved, modellers will continue to rely on heuristics as being representative of observational uncertainty, a strategy that may or may not be valid depending on the case in point. Uncertainty in CDRs also matters for data assimilation. Reanalysis runs of atmospheric forecasting models (e. g. ,  Dee et al. , 2011; Kobayashi et al. , 2015)  provide useful, dynamically consistent information about the climate system over recent decades.
The analyses include inferred fields of variables that are practically unobservable and/or were not historically observed on a global scale. Reanalyses are among the most widely used datasets in geosciences because of their information content and spatio-temporal completeness. Reanalyses are created by data assimilation, which brings observations and models together by using the observations to constrain the evolution of the model towards reality. The combination involves weighting the impact of different observations together and weighting the influence of observa- were the dark target AOD dataset (collection 5. 1), which was operational in MACC using fixed (generic) uncertainty estimates of 0. 1 over land and 0. 05 over ocean. These values were chosen after bias correction and thorough testing of alternative uncertainty assumptions  (Benedetti et al.
, 2008). The AATSR dataset was from Aerosol CCI, and its pixel-level uncertainty estimates were used (with no bias correction). The improved agreement in aerosol distribution suggests that the use of pixel-level uncertainties is beneficial. tions relative to the internal evolution of the model. Ideally, uncertainty estimates should be available for each observation so that more certain observations have more influence on the analysis. Densely sampled, numerous data (such as from satellites) can inappropriately overwhelm other observations if these data are subject to errors that correlate across space and time and therefore do not "average out". Ideally, spatio-temporal correlation should be understood and represented in the observational covariance matrices that weight satellite observations to avoid undue influence on the analysis.
Uncertainty describes the "doubt" we have about the measurand value, given the result of a measurement and our estimate of the error distribution. A classic question at a scientific meeting is the following: "What is the error in your measurement?" This is perhaps asked after a plot has been presented without "error" bars. The questioner is asking for information about uncertainty, but the technically correct answer to this question would be "I don't know the error, and if I did, I would correct for it". Note that these technical definitions correspond well to the plain meaning of the words "error" (mistake) and "uncertainty" (doubt) as used by non-scientists. In addition to improving communication between scientists, careful usage will help scientists communicate beyond their community. It is common for satellite datasets to include quality flags as a simple means to guide users in the usability and validity of data.
6. This brightness temperature is input to processing software that retrieves a geophysical variable to generate a CDR. This sixth step can itself be decomposed into many transformations and dependencies. a. Auxiliary information is also accessed by the processor, which may include a wide range of information. Some information is intrinsic to the observation and is highly certain (e. g. , satellite view zenith angle, time). External geophysical datasets may be used, such as numerical weather prediction fields or surface classification, and these may or may not be provided with quantified uncertainties. All auxiliary information influences the CDR and gives rise to uncertainty. b. The processor typically involves a step to determine that the pixel properties are valid for the intended retrieval (screening cloudy pixels, for example).
This influences the CDR through the sampling distribution of the observations. c. The set of observations is inverted to obtain an estimate of a geophysical quantity, such as an ECV. This inversion may be sensitive to the auxiliary information and may vary in its complexity and degree of non-linearity. d. A multi-mission CDR is created from datasets for several similar sensors by harmonizing discrepancies between sensors (using sensor overlap periods or other means), which modifies the datum to its final value. e. Many ECV estimates may be aggregated to a coarser space-time grid for the purpose of (for example) evaluating the results of a climate model run. Every step in the above sequence is a transformation subject to effects that introduce errors. Characterizing these effects is the significant core work required to develop good uncertainty information in a CDR.
Systematic effects cause errors with structure across a whole dataset, or at least across large space scales and long timescales within a dataset. The term "systematic error" is sometimes loosely equated to "bias", but the concept of a systematic effect is in truth more subtle, since a systematic effect can produce zero-mean errors with no bias overall. Systematic effects can be defined as those that cause errors which one could in principle correct given the necessary quantitative understanding. For example, a CDR may be derived from a series of sensors with differing calibrations. Even if the series is adjusted to compensate for inconsistency between the calibration of different sensors (step 6d), there is uncertainty in doing this; errors in the adjustment parameters potentially affect the entire data record from a particular sensor.
If the uncertainties and variability are correctly quantified, this ratio will be normally distributed with a standard deviation equal to unity. The better the quality of the reference data (the smaller u ref ) and the better the match of satellite to validation data (the smaller u mis ), the more sensitive the validation of u sat. An example validation of uncertainty based on this principle is shown in Fig. 5. In this case, the data are cloudtop height (CTH) from Cloud CCI retrievals driven by an interpretation of the cloud top temperature in thermal imagery and matched to independent CTH measurements made by CALIPSO using laser ranging. The CALIPSO validation data have, in this case, negligible uncertainty; mismatch uncertainty is also neglected. The plots therefore show the histogram of discrepancy in CTH between the two observations divided by the uncertainty estimated in the Cloud CCI retrieval process.
, 2017). For categorical ECVs, such as land cover type, a degree of uncertainty validation can be obtained by verifying that the estimated misclassification rates in the product are stable with respect to reasonable ranges of classification parameters. For instance, if classification is based on training a classifier using a dataset split into calibration and validation ("train" and "test") subsets, the process can be repeated many times with a different random division into train and test subsets, which allows the dispersion in the misclassification rates to be characterized.
Presenting uncertainty information in climate datasets When determining how uncertainty information is to be included in the CDR, various requirements can conflict (Table 2). The core conflict is between providing for applications that require only summary information that discriminates more and less uncertain data, and providing for applications that demand detail about uncertainties that is sufficient to calculate uncertainty in the quantities derived from the CDR (averages in space and time, temporal differences, integrals, trends, and fluxes). Data producers themselves are users of their low-level (e. g. , full resolution, orbital) products when they create higher-level products (e. g. , gridded datasets and gap-filled analyses). In order to provide realistic uncertainty information at the higher level, they may require finegrained uncertainty information for the low-level CDR, such as separate quantification of uncertainty at the pixel level from effects with distinct spatio-temporal correlation properties.
The originators are clear that the purpose of assessing a CDR system against the SMM is to identify priorities for investment in developing a CDR in support of routine climate information and assessments. The overall maturity score is not an indicator of the scientific value of a dataset, which could be very high for a new variable obtained by a system with low maturity. For multiple factors in CDR generation, the SMM maps the status of a CDR system on a scale from 1 (low maturity) to 6 (high maturity). The content of the SSM relevant to uncertainty, validation, and quality is reproduced as Table  3. A score of 2 on the uncertainty quantification criterion corresponds to the provision of limited information, such as estimates of uncertainty that are generic (i. e. , that describe the typical uncertainty for the dataset as a whole).
At the next maturity score, the provided information is still at the level of the dataset, but it is comprehensively described and quantified, which suggests that the nature of the effects causing error is determined. To move to a score of 4, this understanding is applied to develop uncertainty information in the product that is specific to each datum and capable of discriminating between more and less certain data. A score of 5 corresponds to providing a quantification of the correlation structures in errors via covariance information or other means. For practical purposes, since covariance matrices can be large, this provision is not necessarily required to be within the product per datum. However, feasible approaches may be found that satisfy this maturity criterion at a per-datum level, such as the decomposition of total uncertainty into dominant components arising from effects with distinct, quantified correlation structures (e.
g. ,  Bulgin et al. , 2016b). The highest maturity score of 6 is obtained when the estimated uncertainty magnitudes and error correlation structures are thoroughly validated. It is not the purpose of this paper to discuss the general merits of the maturity matrix approach to evaluating CDR systems. However, it is clear that if CDR producers address uncertainty using the perspectives in this paper, they will achieve a high maturity score in this aspect of the SMM. This paper has demonstrated the complexity of developing good uncertainty information for climate dataset users. The aspiration to provide per-datum uncertainty estimates at all product levels and all product versions at all spatio-temporal scales is very challenging and not fully achieved. It is clear that developing and validating uncertainty estimates involves effort comparable to developing the retrieval itself.
There is a lot of diversity in the nature of CDRs and the errors present in them. The details for good practice in describing the uncertainty in CDRs vary accordingly. Nonetheless, it is useful to state some general principles that emerge from the previous sections. 1. Include quantitative uncertainty information within the dataset. (Don't expect users to find uncertainty information by reading related papers. ) 2. Follow metrological practice for quantifying uncertainty. The baseline good practice is to provide the total standard uncertainty for numerical variables. 3. Uncertainty estimates (or the means to calculate them) should be provided per datum in CDRs for which uncertainty varies significantly so that the uncertainty information discriminates which data are more and less certain. 4.
Assuming per-datum uncertainty information is provided, avoid redundancy of this information with quality flags. Do not flag high-uncertainty data as "bad" if a valid estimate of that high uncertainty is provided; instead, use quality flags to indicate the level of confidence in the validity of the provided uncertainty and retrieval assumptions. 5. Define what uncertainty information is given in the CDR in the product documentation. 6. Describe in the product documentation the main effects causing errors, how uncertainty varies within the dataset, how errors may be correlated in time and space, and under what circumstances estimated may be invalid (and flagged as such). 7. Use validation to evaluate both retrieved quantities and associated uncertainty estimates. 8. Propagate uncertainty appropriately (accounting for error correlation) and consistently when creating aggregated products.
05 • grid) and at pixel level in gridded noise through retrieval pro- variables (temperature, averaged (0. 5 • , daily, swath product and averaged cess; (optimal estimation) height, fraction) which are monthly) estimates for each cell in gridded context-specific (surface) continuous non-negative products estimate of retrieval un- variables certainty; propagation of uncertainty to grid boxes accounting for correlation Glaciers Glacier outlines derived Outlines are provided in Not regularly determined; Various methods, including from optical satellite data a vector format, scene by some tests have been pub- multiple digitizing by ana- with manual intervention scene; a geospatial database lished lysts; appropriate validation addresses 200 000 glaciers data are generally missing globally Greenhouse XCO 2 and XCH 4 are de- One file per day, including Standard uncertainty of Propagation of sensor noise gases (XCO 2 , fined as atmospheric, dry- XCO 2 and XCH 4 , plus ad- XCO 2 and XCH 4 (per (and a priori uncertainty) XCH 4 ) air, column-averaged mole ditional information for sur- sounding) plus averaging through retrieval process fractions of CO 2 and CH 4 face flux inversions; sound- kernels (AK) and a priori and error scaling to match ings have surface footprints concentration profiles validation statistics of ∼ 10 to ∼ 60 km depend- ing on sensor Land cover A categorical variable de- Annual land cover maps at Class uncertainty is avail- Class uncertainty is com- scribes the terrestrial sur- 300 m depicting land cover able at the map and class puted from confusion ma- face annually in 22 dis- change from 1992 to 2015 level; standard uncertainty trix built on independent crete classes (from the UN of composite surface re- statistical validation pro- Land Cover Classification flectance is provided at cess System) pixel level Ocean colour Variables of bio-optical rel- Chlorophyll a concentra- Standard uncertainty and Uncertainty assignment evance with high dynamic tion, spectrally resolved in- bias estimates for all prod- based on product compar- range (4 decades) herent optical properties, ucts except backscattering ison with match-up in situ diffuse attenuation coeffi- coefficient data for each optical class, cient at 490 nm, member- applied per pixel according ship of optical classes to class membership Ozone Ozone total-column and Ozone profiles from limb Standard uncertainty esti- Measurement noise propa- vertical profiles sounders with ∼ 3 km verti- mates are given for each gated through the retrieval cal and ∼ 300 km horizon- ozone value in each record process and to the higher tal resolution; ozone pro- levels of data products, ran- files from nadir sounders domly varying parameter with ∼ 4 km vertical resolu- errors; sampling uncertain- tion; analysed and gridded ties versions of profiles and to- tal column.
is inconsistent with best practice. Work with community standards to converge practices. 3. Provide sufficient uncertainty in- Uncertainty components from errors with dif- Increases data volume (cf. 1). Increases com- formation to allow correct propa- ferent spatial correlations need to be separately plexity of dataset (cf. 2). Could provide two gation of uncertainty to spatial and quantified with correlation information (e. g. , versions of data, one with summary and the temporal averages of data. length scales, covariance matrix). other with comprehensive uncertainty informa- tion, with guidance as to which is needed for different purposes. 4. Provide information about Information is provided on temporal correlation Full spatio-temporal covariance matrix for CDR temporal stability of observations of errors, particularly arising from long-term is challenging to calculate or parameterize and and/or evolution of trend uncer- systematic effects.
02 amu and the dwell time for all elements was 10 ms. Measurements were carried out with collision cell in either -3. 0 V mode (In, Sc, Cr, Fe, Ni, Cu, Cd) or 0. 0 V mode (Sc, Al). H 2 /He (7% v/v) was used as the collision gas with 5 ml/min flow rate. Data were processed with PlasmaLab 2. 5. 11. 321 (Thermo Scientific, Bremen, Germany).
The instrument was calibrated prior to each measurement with an external ProteoMass ™ MALDI Calibration Kit (Sigma Aldrich, Munich, Germany). Data were processed using the flexControl 3. 4 and flexAnalysis 3. 4 software (Bruker Daltonik, Bremen, Germany). Synchrotron FTIR microscopy. FTIR microscopy analyses were performed at beamline ID21 at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France  44. The beamline is equipped with a Thermo Nicolet Continuum (Thermo Scientific, Madison, WT, USA) microscope coupled to a Thermo Nicolet Nexus FTIR spectrometer (Thermo Scientific, Madison, WT, USA) with a 32x objective, a motorized sample stage, and a liquid nitrogen-cooled 50 µm HgCdTe detector. Maps were acquired in transmission mode using a 10 × 10 µm² beam, step size of 8 µm.
Spectra were recorded as an average of 64 scans per spectrum, over a range of 4000 to 850 cm -1 and with a spectral resolution of 4 cm -1. The OMNIC software was used to transform spectra from maps of skin and lymph node samples to second derivatives using Savitsky-Golay of second polynomial order with 21 smoothing points  45, 46. Unscrambler X software (Version 10. 3, CAMO Software, Oslo, Norway) was used for further statistical analysis. Principal component analysis (PCA) was performed on the mean-centered data using the spectral regions from: 1800 to 1350 cm -1 (related to proteins) and 3200 to 2800 cm -1 (related to lipids)  47, 48. PCA was performed separately for skin and lymph node samples. Score plots and loading plots obtained by PCA analysis as well as mean values from the regions of interest were used for data interpretation.
Acknowledgements The authors thank the  ESRF  for allocated beamtimes on ID21 and  ID16B. N. Dommershausen  is acknowlegded for technical help with LDI-ToF-MS analyses. We are grateful to  Prof. Dr. Bäumler  for valuable discussions. This work was supported by the intramural research project (SFP # 1322-604 ) at the  German Federal Institute for Risk Assessment (BfR).
innexi, it is possible that X. innexi does encode fewer rRNA gene copies, but this conclusion awaits further investigation. The draft genome of X. innexi encodes the same number (79) of tRNAs as do the complete genomes of X. nematophila and X. bovienii. To investigate the virulence coding potential of the X. innexi genome, CDS protein sequences were analyzed using similarity to known virulence factors and conserved protein domains (Table  4 , see Methods for details)  [41]. In addition to these direct searches, we used the MicroScope Gene Phyloprofile tool  [42]  to identify sets of genes specifically absent in X. innexi genome (Additional file 3). We used loci present in the completely sequenced genome of the virulent strain X. nematophila (ATCC 19061) and identified those with homologs in the genomes of the virulent strains X.
The X. innexi genome contains 4 contigs with regions that have similarity to MARTX-encoding genes, based on a BLASTp search with XNC1_1381 (Fig. 4 ). In the assembly, only one gene (XIS1_650005) encodes a full suite of A, B, and C repeats and the effector domains. However, based on alignment with the X. nematophila and X. bovienii MARTX proteins this protein lacks A repeats 4 through 8 (of 14) (Fig. 4  and Additional file 5). In addition to XIS1_650005, we identified another five regions with one or more MARTX protein-encoding domains. Of these, two (XiS1v1_640001 and XIS1_650001) are predicted to encode an A domain region that, like   XIS1_650005, lack repeats 4 through 8, as well as a truncated B domain region (extending up to repeat 16 of 38) that lack repeats B1-B6 (Fig. 4  and Additional file 5).
For instance, in the genome of the highly virulent Xenorhabdus nematophila ATCC19061 strain, only hemolysin and DUF637 domain clusters are represented (Fig. 5 ; Additional file 6). Another class of secretion systems that can be involved in virulence is the T6SS. These are bacterial nanomachines comprising 13 conserved structural proteins, which deliver toxic effectors into eukaryotic or prokaryotic organisms in a one-step firing mechanism  [62]. T6SSs often are associated with roles in virulence and inter-bacterial competition, providing a selective advantage against competitors  [63]. To analyze the T6SS content in the draft genome of X. innexi, we used a combination of the Magnifying Genomes server (MaGe) and NCBI Conserved Domain Database and identified three T6SS clusters, T6SS-1,2, and 3 (named in order of their appearance in the draft genome) (Fig.
One such group of T6SS effectors is the polymorphic toxins, Rhs proteins. Rhs proteins containing PAAR domains have been reported as T6SS-dependent antibacterial effectors that mediate both intra-and inter-species competition  [65]. Rhs proteins, in human pathogens such as Pseudomonas aeruginosa and Enterobacter cloacae, mediate bacterial competition under in vitro conditions. For these pathogens, Rhs proteins may play an important role in virulence by establishing a suitable niche for survival during infection of the host  [66]. Genes encoding known or putative T6S effectors, including Rhs proteins, are often found near vgrG genes and require the cognate Vgr for T6S secretion  [67]. X. innexi strain HGB1681 encodes ten YD-repeat (PF05593) proteins, five of which are putative Rhs proteins based on the presence of characteristic YD-repeats, a rhs core domain flanked by conserved motifs, and a variable C-terminal 'tip'  [68]  (Fig.