dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, these data are not included in HARMOZ. The obliquity is defined as the angle between the local vertical and the trajectory of the tangent point within the atmosphere  (Gurvich and Brekhovskikh, 2001) ; it is 0 • for in-orbital-plane occultations. The vertical sampling is denser in oblique occultations. Thanks to the Tikhonov-type targetresolution regularization  (Kyrölä et al. , 2010; Sofieva et al. , 2004)  and accurate parameterization of modeling errors in the GOMOS v6 algorithm  (Sofieva et al. , 2009 (Sofieva et al. , , 2010)) , the quality of GOMOS ozone profiles and uncertainty estimates practically does not depend on this parameter. Very oblique occultations (with obliquity larger than 85 • ) are not present in the harmonized dataset. The profiles of the normalized χ 2 statistics indicate the quality of retrievals and the quality of error estimates (normalization is on the number of spectral channels minus the number of fitted parameters, for exact definition of this parameter see e.
g. ,  Sofieva et al. , 2010). Usually, χ 2 norm is very close to 1, thus indicating the proper characterization of uncertainties. However, there are occultations in the GOMOS dataset that have very large χ 2 norm values. They usually correspond to the locations over the South Atlantic Anomaly (SAA), which are affected by energetic particles. From the harmonized dataset, such data are also excluded. The SAA flag presented in the list of optional parameters has been computed based on the position of the satellite. Therefore, it does not reflect the real quality of the ozone data; it is presented for information only. The illumination flag indicates the illumination conditions (full dark limb, straylight, twilight). The bright-limb occultations (with illumination = 1) are not present in the harmonized dataset.
0625 cm -1 (unapodized) since then, while the spatial sampling could be improved to 1. 5-2 km tangent altitude step width in the stratosphere, and an along-track sampling of 410 km (550 km before). The finer vertical sampling allowed for better vertical resolution of the MIPAS products. However, this has the shortcoming of a not fully consistent data record before and after the change of observation mode. Currently, only data from the second period since 2005 are included in the harmonized dataset. There exist four MIPAS Level 2 processors: the operational ESA processor and three independent research processors hosted by ISAC-CNR/University of Bologna, Oxford University and KIT IMK/IAA, respectively. All four processors use the same Level 1b data, but the Level 2 algorithms are different. In the framework of Ozone_cci project, ozone profiles retrieved by the four MIPAS processors were compared.
For creating the harmonized dataset, the MIPAS data were taken from the best performing pro-cessor under this comparison, which is the KIT IMK/IAA version V5R_O3_220/221 research processor (in particular, this processor has shown the best performance in the upper troposphere and lower stratosphere in comparisons with ozonesondes and lidars). The results of the processor intercomparison are described in detail in  Laeng et al. (2013). The description of the KIT IMK/IAA processor can be found in von  Clarmann et al. (2003 Clarmann et al. ( , 2009)). The dataset has been extensively validated  (Laeng et al. , 2012 (Laeng et al. , , 2013)). The following filtering was applied to the retrieved data in order to ensure the good quality of the profiles. -The data for which the diagonal value of the averaging kernel is less (in absolute value) than 0.
03 are considered unreliable and are discarded, because this corresponds to a local altitude resolution exceeding approximately the entire altitude coverage of the profile. This filter shall guarantee that only data are used which contain at least a minimum of measurement information. -Data in parts of the atmosphere that lie below the lowermost useful tangent altitude are discarded, as they do not contain measurement information. This applies in particular to cases when the spectra measured at low tangent altitudes are discarded due to cloud contamination. For interpolation to the HARMOZ pressure grid and for conversion to ozone concentration units, MIPAS retrieved temperature and pressure profiles have been used. The ECMWF temperature profiles at measurement locations are included as optional parameters.
The pointing accuracy for the entire limb scan is estimated to be about 200 m  (Bramstedt et al. , 2012) , while the relative pointing error between different tangent heights is negligibly small. An alternative retrieval of ozone profiles from SCIA-MACHY limb observations is provided by the European Space Agency/DLR  (Doicu et al. , 2007). The original retrieval was based upon retrievals from visible wavelengths only covering the Chappuis ozone absorption bands. This limits the retrieved altitudes to below 40 km compared to about 65 km in the SCIAMACHY-IUP retrieval. SCIAMACHY provides stratospheric ozone profiles with vertical resolution of 3-5 km and estimated precision of 10-15 %  (Rahpoe et al. , 2013). Ozone data are usually of poor quality in cloudy conditions. Therefore, data at altitudes contaminated by clouds are filtered out in the harmonized dataset.
OSIRIS OSIRIS on board the Odin satellite has been taking limbscattered measurements of the atmosphere from 2001 to present. It operates at wavelengths of 280-810 nm. For the harmonized dataset, the OSIRIS SaskMART v5. 0x ozone data  (Degenstein et al. , 2009; Roth et al. , 2007) , which is retrieved using the SASKTRAN spherical radiative transfer model  (Bourassa et al. , 2008) , have been used. The data have been filtered for outliers according to the techniques described by  Adams et al. (2013a). This involves a threestep process: (1) radiances are screened for evidence of clouds and energetic particles; (2) retrieved ozone profiles are screened using statistical techniques; and (3) retrieved ozone profiles are assessed visually (for details, see Appendix A in  Adams et al. , 2013a). OSIRIS provides stratospheric ozone profiles with vertical resolution of 2-3 km and estimated precision of 2-10 %  (Bourassa et al.
, 2012). During inter-comparisons with other satellite and in situ measurements  (Adams et al. , 2013a, b) , it was found that agreement between OSIRIS and the validation datasets depends on the OSIRIS optics temperature, retrieved aerosols and albedo. These are included as optional parameters in the OSIRIS harmonized dataset. For interpolation to the HARMOZ pressure grid, we used the ECMWF altitude-pressure profiles at measurement locations.
SMR SMR on board the Odin satellite has been measuring thermal emissions since 2001 to present. For SMR, the version 2. 1, 501. 8 GHz band retrievals are presently (April 2013) recommended for use. SMR measures ozone also independently in other bands and results from these bands will be included at a later stage. The Level 2 products from the 501. 8 GHz band provide ozone data in the ∼ 12-60 km altitude range (above 17-18 km at midlatitudes) with 2. 5-3. 5 km vertical resolution and a single-profile precision of about 20 %. The systematic error is estimated to be smaller than 0. 75 ppmv (e. g. ,  Urban et al. , 2005 Urban et al. , , 2006)). Note that measurements in this observation mode were carried out every third day until April 2007 and every other day thereafter. The thermal emission technique allows ozone to be measured during day and night.
Global fields between ∼ 83 • S and ∼ 83 • N are typically produced from 14-15 orbits per observation day based on up to 65 limb scans per orbit. Since they are derived from a relatively weak spectral line, individual ozone profiles are quite noisy but averages agree reasonably well with correlative measurements (e. g. ,  Dupuy et al. , 2009; Jégou et al. , 2008; Jones et al. , 2007). For HARMOZ, SMR data with a quality flag equal to 0 or 4 and a measurement response larger than 0. 67 have been used. A filtering of outliers (also using data from other simultaneously retrieved species such as N 2 O and ClO) has also been applied. For SMR, vertical resolution is estimated from the full-width-at-half-maximum (FWHM) of averaging kernel functions calculated (offline by the retrieval algorithm) for 4 observation days in 2010 (mid-March, mid-June, mid-September, mid-December).
ACE-FTS ACE-FTS is a solar occultation instrument that records spectra between 2. 2 and 13. 3 µm (750-4400 cm -1 ) at a high spectral resolution of 0. 02 cm -1  (Bernath et al. , 2005). ACE-FTS provides retrieved altitude profiles of the volume mixing ratio (vmr) of ozone on a 1 km native grid. Each measurement is made at the time of local sunrise/sunset. ACE-FTS provides stratospheric ozone profiles with vertical resolution of ∼ 3 km and estimated precision of 1-3 %. For HARMOZ, the version 3. 0 processed data from March 2004 to September 2010 have been used. These data have been filtered to only include data points that are within 5 median absolute deviations (MADs) from the vmr median. Additionally, any data point for which the vmr error exceeds the measurement has also been excluded. The temperature profiles, which are included in the netCDF files, are determined in two parts.
Data format and availability The harmonized dataset is provided in netCDF-4 format (with README file) and can be found at the Ozone_cci web page http://www. esa-ozone-cci. org/?q=node/161 (or doi:10. 5270/esa-ozone_cci-limb_occultation_profiles-2001_2012-v_1-201308). It consists of folders corresponding to satellite and instrument. Each folder contains monthly data files with self-explanatory names. For example, the file ESACCI-OZONE-L2-LP-GOMOS_ENVISAT-IPF_V6-200801_fv0004. nc contains GOMOS ozone profiles for January 2008. The parameters in the files are compliant with CF-1. 5 convention. Sample scripts to read the netCDF-4 files with MATLAB, IDL and IGOR Pro are also available on the web page.
Data agreement tables To quantify the agreement between the individual datasets in HARMOZ, tables of experimental biases between each pair of instruments are provided, as well as the uncertainty of the bias estimates. The data agreement tables are computed using collocated measurements with the following restrictions on time difference ∆t, distance between tangent points ∆d, and latitude difference ∆θ: i. standard collocation: |∆t| ≤ 24 h, |∆d| ≤ 1000 km, |∆θ| ≤ 2 •. ii. tight collocation: |∆t| ≤ 4 h, |∆d| ≤ 400 km. The tight collocation criterion is based on the effective horizontal resolution of the considered limb/occultation measurements, and can be considered therefore as the "natural" collocation criterion. The standard criterion ensures a sufficient number of collocated measurements and thus provides reliable bias estimates with better seasonal and latitudinal coverage.
This assumption is appropriate for nearly all HARMOZ pairs due to the properties of data and the method for selecting the collocated measurement. For MIPAS-SCIAMACHY collocations, deviations from assumption are possible; this problem is discussed in  Toohey and von Clarmann (2013). The bias is evaluated over the common altitude range of the pair of instruments, using concentration profiles. The bias is evaluated for each month in 20 • latitude zones from 90 • S to 90 • N. The bias tables are structured in 15 folders corresponding to the instrument pairs, e. g. , "GOMOS_OSIRIS". They can be found at the same web page http://www. esa-ozone-cci. org/?q=node/161 (or doi:10. 5270/esa-ozone_cci-limb_occultation_profiles-2001_2012-v_1-201308). The folders contain bias tables corresponding to each month in netCDF-4 format.
The file names contain information about the year and the month, as well as the instruments and the collocation type. For example, the file ESACCI-OZONE-AgreementTable_GOMOS_OSIRIS_200801. nc contains the bias table between GOMOS (x 1 ) and OSIRIS (x 2 ) for January 2008, for the standard collocation criterion. The files for tight collocation criterion end with "_tight. nc". The parameters included in the netCDF files are presented in Table  5. A sample visual representation of the main bias parameters is shown in Fig. 4. The data agreement tables present experimental estimates of biases and their uncertainties. At some locations and seasons, the estimated biases can be not statistically significant. Furthermore, discrepancies at upper altitudes are expected (and observed), because of diurnal ozone variations (e.
The weighted mean (with uncertainties of individual biases) is used for averaging of monthly bias values. Since the number of collocations with MIPAS over the considered two years is large, the observed differences are statistically significant. Two features can be easily noticed in Fig. 6. First, MIPAS is biased high at altitudes of 40-45 km with respect to all instruments. This feature has also been noticed in MIPAS validation studies  (Laeng et al. , 2012 (Laeng et al. , , 2013)). Second, a visible enhancement in SCIAMACHY data in the equatorial atmosphere at ∼ 30 km is observed. This feature is unique for SCIAMACHY and also has been observed previously  (Mieruch et al. , 2012). The presented examples are only simple illustrations of possible analyses using the harmonized dataset and the data agreement tables.
Summary We have described the HARMonized dataset of OZone profiles (HARMOZ) based on limb and occultation data from six satellite instruments: GOMOS, MIPAS, SCIAMACHY, OSIRIS, SMR and ACE-FTS. The main strength of HAR-MOZ is that it is user-friendly: the independent datasets from individual instruments, which passed thorough quality control, are presented in the same form as far as possible (common vertical grid, common parameters, and a common data format). Although the datasets are simple and user-friendly, they are also comprehensive: all important parameters attributed to individual datasets are presented. Quality of ozone profiles in HARMOZ is characterized by uncertainty estimates and the vertical resolution. The created data agreement tables provide ready information for various bias and drift analyses.
This information is of high importance in joint data analyses and in data merging. A detailed analysis of biases and drifts between the Ozone_cci instruments will be the subject of a future publication. Between the various datasets, there are ozone measurements available for all seasons, various times of day, and good latitudinal coverage. This user-friendly dataset can be very interesting and useful for different analyses and applications, such as data merging, data validation, different intercomparisons, data assimilation and scientific research. The system has been designed in a flexible and open way in order to facilitate the ingestion of additional datasets from other instruments and/or from other processors, when these become available.
, 2005; Brayton et al. , 2007). Other neglected tropical diseases such as Chagas disease , Human African Trypanosomiasis and Leishmaniasis are caused by parasites in the Excavate supergroup  (Trypanosoma cruzi, Trypanosoma brucei and Leishmania spp. ). New effective drugs are needed to fight these parasites and determining which proteins may be targatable is one of the first steps. Other than a few species specific predictors such as PATS  (Zuegge et al. , 2001) , PlasmoAP  (Foth et al. , 2003)  and PlasMit  (Bender et al. , 2003) , which are specialised for Plasmodium falciparum proteins, very few predictors are suitable for use within these groups. These species specific predictors do not generalise well to other species and also have the disadvantage of being trained on very small datasets.
Datasets We generated our training and test set from Swiss-Prot Release 2011 02. We found 129,516 eukaryotic sequences with a "SUBCELLULAR LOCATION" annotation. We removed sequences that had non-experimental qualifiers (Potential, Probable, By similarity) or were less than 30 or greater than 1500 residues in length, leaving 47,521 sequences. We internally redundancy reduced this set using an all-against-all BLAST  (Altschul et al. , 1997)  search (with e = 10 -3 ) removing any sequence with a hit with more than 30% sequence identity to any other sequence in the set (Table  1 ). Proteins were then classified as secreted (having the keyword "Secreted" within their "SUBCEL-LULAR LOCATION" annotation, and not "membrane" or "intermembrane"), membrane (having the keyword "membrane" or "intermembrane" within their "SUBCELLULAR LOCATION" annotation and not "Secreted"), or "other" (not "Secreted", "membrane" or "intermembrane").
We created an independent test set from Swiss-Prot Release 2012 07 using a similar method. We redundancy reduced this set with respect to the training set to less than 30% sequence similarity and then internally redundancy reduced the remaining sequences to less than 30% sequence similarity, leaving 715 protein sequences (Table  1 ). We refer to this dataset as the independent test set. We further reduced the independent test set for use only when benchmarking against other predictors. We only included cell membrane proteins in this dataset to be fair to the other predictors, some of which were trained only to identify cell membrane, and not other membrane, proteins. We further redundancy reduced this dataset with respect to the LocTree2 training dataset in order to make a fair comparison between SCL-Epred and LocTree2.
Results and Discussion SCL-Epred performs well when tested in 10-fold cross-validation on the training set of 15,202 protein sequences and on the 715 protein sequences of the independent test set (Table  2 ), predicting sequences into three classes: secreted, membrane and "other". The number of correctly predicted sequences (Q) is 86% and 83. 2% respectively for the training and independent test sets. However, if the class size is unbalanced, as it is here (secreted sequences make up only 13% of the dataset), Q can be misleading, as if a predictor predicts all sequences into the largest class a reasonable Q can still be achieved. For example, a Q of 60% could be achieved on our independent test set by predicting all sequences into the "other" class, at the expense of predicting every secreted and membrane protein incorrectly.
We generated three test sets of sequences which were excluded from our dataset due to having multiple subcellular location annotations (membrane and cytoplasm; membrane and secreted; and secreted and cytoplasm). We internally redundancy reduced this dataset as previously described, and redundancy reduced it to less than 30% sequence similarity with the training set. When submitted to the SCL-Epred web server 31 of the 32 of membrane/cytoplasm sequences were correctly predicted as either membrane or "other" and all 5 of the secreted/cytoplasm sequences were correctly predicted as secreted or "other". There was only one membrane/secreted sequence which was predicted as "other". We benchmarked SCL-Epred against a number of publicly available web servers using the benchmarking subset of the independent test set of 562 protein sequences.
, 2004). For predictors which predict into more than two classes we classified predictions as secreted (secreted-LocTree2/Extracellular-ESLpred2/Secretory(SP)-SLPFA)) or "other". ESLpred2 with a Q of 81. 7% only predicted four of the secreted proteins correctly. However, a GC of 0. 13 more accurately reflects the poor performance of ESLpred2 on this dataset (Table  3 ) which demonstrated the importance of not relying on Q as an overall indicator of performance. SCL-Epred has the highest Q and GC, however LocTree2 and SignalP also perform very well (all Q > 90% and GC > 0. 7 ). We tested the performance of a two class consensus predictor made up of SCL-Epred, LocTree2 and SignalP, where a sequence was predicted as secreted if more that one of the three predictors predicted it as secreted, and predicted as non-secreted otherwise.
We wished to investigate if SCL-Epred could accurately predict the location of proteins sequences of species outside of the large plant, animal and fungi groups for which there are many specialised predictors. As the number of sequences annotated with a subcellular location outside of these groups is limited, species specific predictors for non plant, animal or fungi groups have the disadvantage of being trained on very small datasets. Therefore, having trained SCL-Epred using all available eukaryotic sequences we examined the training set results (in 10-fold cross-validation) categorising each sequence into one of five supergroups  (Keeling et al. , 2005) , as follows, to assess the predictive power for each supergroup. We found 723 unique species in the training set. First we grouped these sequences by their UniProt taxonomy (Table  5 ), and then organised them into one of the five supergroups: Unikonts (Amoebozoa, Fungi and Metazoa), Plantae (Viridiplantae, Rhodophyta and Glaucocystophyceae), Chromalveolates (Stramenopiles, Alveolata, Cryptophyta and Haptophyceae), Excavates (Diplomonadida, Heterolobosea, Jakobida, Parabasalia and Euglenozoa) and Rhizaria.
As the number of non-Plantae/Unikont sequences was still quite small we grouped the SAR group (Rhizaria and Chromalveolates) and Excavates together. Table  6  shows that 85% of the dataset sequences are Unikonts, with only 2% coming from the Chromalveolates/Rhizaria /Excavate supergroups. However, even with only such a small number of sequences SCL-Epred has some predictive power with a Q of 75. 1% and a GC of 0. 56. Importantly, the predictive power is balanced across all three classes with MCC only varying from 0. 53 to 0. 57. The real strength of the predictor, nevertheless, still lies within the Unikonts which have a very strong performance with a Q of 86. 4% and a GC of 0. 77. We then compared the performance of SCL-Epred on the SAR Excavate dataset to that of LocTree2, Euk-mPLoc and Cello (Table  7 ).
SCL-Epred is more accurate than the other predictors across each of the three classes except for the "other" class where the MCC for LocTree2 is better. GC and Q are better in all cases. A consensus predictor which selects the SCL-Epred prediction if the LocTree2 prediction is either secreted or membrane, and keep the LocTree2 prediction if it is "other" is more accurate than either SCL-Epred or LocTree2 across all classes (Table  7 ). It should be kept in mind that the SAR Excavates dataset is a subset of SCL-Epred training data and that the method performs better on the training data than on independent test data (Table  2 ). Second, it may overlap with the training data of other methods. This should be kept in mind when evaluating these results.
Conclusion We have developed a general de novo subcellular localisation predictor for eukaryotic protein sequences which predicts into three classes (secreted, membrane and "other") based on a N-to-1 Neural Network architecture (N1-NN). We have trained SCL-Epred in 10-fold cross-validation on large non-redundant subsets of annotated proteins from Swiss-Prot 2011 02 and benchmarked it against other subcellular localisation prediction servers on an independent test set. SCL-Epred performs favourably on these benchmarks. We have explored the possibility of using a subcellular localisation predictor trained on a diverse set of eukaryotic sequences to predict the localisation of proteins from the often overlooked Chromalveolates, Rhizaria and Excavate supergroups. We have shown that SCL-Epred has some predictive power here and expect that as larger datasets become available these will to be especially beneficial towards improving prediction accuracy for these sequences.