dataesr/scientific-paragraphs-categorization huggingface dataset:
Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across a multitude of websites often with inconsistent metadata and sparse information relating to the quality, accuracy and validity of the data. Subsequently, the task of comparing datasets to decide which is the most appropriate for a certain purpose is very complex and often infeasible. In support of the European Union's Copernicus Climate Change Service (C3S) mission to provide authoritative information about the past, present and future climate in Europe and the rest of the world, each dataset to be provided through this service must undergo an evaluation of its climate relevance and scientific quality to help with data comparisons.
This paper presents the framework for Evaluation and Quality Control (EQC) of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store (CDS). The EQC framework will be implemented by C3S as part of their operational quality assurance programme. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and a more in-depth evaluation of a selection of 24 individual data products derived from satellite and in situ observations across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. A prototype Content Management System (CMS) to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users is also described.
Further to the basic provision of these data streams there is a critical need for comprehensive metadata on the quality of the data to enable users to judge the fitness for use and ensure confidence in the information used to support decision making processes. A rigorous quantification of the accuracy and validity of climate information from EO satellite and in situ observations is fundamental to the scientific understanding of the Earth system and its response to change and progress in policymaking. Furthermore, attention must be paid to how this quality information is provided to data users  [3] [4] [5] [6]. The Copernicus Climate Change Service (C3S), implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) is one of six operational environmental information services established by the European Commission (EC) within the Copernicus Earth Observation Programme.
C3S supports climate change adaptation and mitigation in Europe by ensuring reliable access to high-quality data on past, present and future climate, and by enabling users to make effective use of this data, e. g. , for monitoring climate change and its impacts, for developing climate services in various industrial sectors, and for policy development and implementation. The backbone of C3S is a cloud-based Climate Data Store (CDS,  [2, 7] ), which aims to make it easier for users with varying backgrounds to access complex climate datasets and turn them into useful information products. The CDS represents a single point of access to a catalogue of climate datasets including Essential Climate Variable (ECV,  [8] ) products derived from observations, model-based climate reanalyses, seasonal forecast data products, and climate model simulations including projections.
In a progressive commitment to ensure that all datasets available through the C3S CDS are traceable, adequately documented and accompanied by quality information so that data users can make informed decisions for their application, C3S has made significant investments in the ongoing development of Evaluation and Quality Control (EQC) functionality. The purpose of the EQC is to collate and display quality assurance (QA) evidence on each of the individual data sets in a standardized manner allowing impartial evaluation by users, as well as a means for monitoring the status of and improving the climate data service information through time. The QA information collected will be prominently displayed in an accessible format on the CDS webpages associated with each data set in the catalogue. As discussed in  [3, 9] , regulatory frameworks requiring EO data and product producers to be held accountable for ensuring the quality, accuracy and validity of the information provided do not currently exist nor do the standards against which data quality should be monitored.
ECV Product Inventory For a demonstrator set of nine ECVs including: Precipitation; Surface Air Temperature; Leaf Area Index (LAI); fraction of absorbed photosynthetically active radiation (fAPAR); Soil Moisture; Sea Surface Temperature (SST); Ocean Colour; Ozone and Aerosols, an exhaustive inventory of observational data sets (both satellite and in situ) was compiled. Over 500 individual data products were found (Table  1 ). To be considered climate relevant to the C3S in this study, the datasets listed in the inventory had to be global, freely available, operationally produced over a long temporal period (shorter if recently funded for CDR development) and known to be used by the scientific community. Over 250 individual products across the nine ECVs were considered contenders for potential inclusion in the CDS catalogue (Table  1 ).
An initial Quality Assurance Template (QAT) checklist was derived based on previous EU and internationally funded initiatives as well as the user requirements gathering process, to capture the status of several QI's for each of the climate relevant datasets. The initial set of QI's included information about documentation, product generation, quality flags, uncertainty characterisation, validation and inter-comparison  [3]. A top-level evaluation of the QI check list for each of the 250 datasets revealed that most had some sort of documentation about the algorithm development and associated user guide, but many had little in the way of detailed quality assessment or uncertainty characterisation. Furthermore, the presentation of quality information between and within ECV product families was inconsistent, which ultimately hindered the ability to make a sound judgement on the overall quality of each data product.
Further, drop down menus are provided to reduce free text fields and ensure the database operates efficiently. The QAT questions were designed with the aim of encouraging the data producer to not only relate the existing product quality information in the standardised manner, but to contemplate various aspects of product quality that may not have been previously considered. On average it is expected to take a product producer and/or production team, having full scientific knowledge of their data product, approximately three hours to complete the entire QAT. Further, as part of the EQC content management system (CMS) outlined in Section 3. 3, it is anticipated the product details could be imported from existing metadata structures and an autofill On average it is expected to take a product producer and/or production team, having full scientific knowledge of their data product, approximately three hours to complete the entire QAT.
This evaluation indicates the QI sections where sparse information was provided across both datasets, potentially highlighting a scientific knowledge gap to be explored through further funding, as well as QI categories in which more information has only been provided for one product indicating a more in depth assessment of the product quality has been provided by the producer. Similar to the evaluation process developed for QA4ECV  [3] , the EQC evaluation only assesses the fraction of information provided relative to all questions and reviewer assessment of information provided. Four levels of achievement ranging from Basic, Intermediate, Good and Excellent are defined. To achieve a rating of Excellent, almost all QI details per individual section must be provided with substantial credible detail, while a score of Basic would indicate that minimal explanation of a QI was provided and that good practices (if currently available) were not necessarily followed.
Therefore, from a user application point of view the data may be considered of little utility, but from a QA point of view the assurance that best methods have been used to generate the data is high. It is anticipated as the EQC is implemented more broadly for a greater number of data products, and as further funding investments and international community efforts address product quality assessments through validation, inter-comparisons and development of uncertainty characterisation methods and guidance, there will be vast improvements in the understanding of the quality of data products, implementation of good practices and uptake of this by data users. Further, assessment of data products for specific applications is being undertaken as part of the C3S Sectoral Information Systems (SIS) and other EQC contracts.
e. , Table  1 ), the EQC will be applied incrementally. For example, currently EU funded data products will be expected to meet a higher level of quality information provision and evaluation than those data products that are no longer funded but still considered climate relevant, or are from international data providers. This does not necessarily mean they are of lesser quality or climate relevance, but rather more effort may be required to collate and perform thorough quality evaluations of these datasets to meet the C3S highest standard.
In Table  3  we outline the 10 most common and priority science knowledge gaps that will require further research investment to ensure all quality aspects of climate data sets can be ascertained and over time provide users the range of information necessary to confidently select relevant products for their specific application. Recommendations for addressing the science gaps are also provided and are chiefly targeted at data producers and agencies funding CDR product development. However, it is important to note that knowledge of the science gaps and research required to address these gaps is highly relevant to data users who should be aware of data quality issues prior to application of these datasets. Inevitably, it is the data users who will drive the requirement for better data and the provision of quality information with data products into the future.
They are developed through in-kind contribution of a global network of experts for each ECV and can therefore take many years to be produced and made publicly available. Evaluation of the validation methodologies used by different groups, reveals that certain sources of error are not being included in the analysis. Often the uncertainty in the reference (in situ or field measured) data is not included, though in part this may be because many reference sources still do not have accurate uncertainty estimates to be used. Representativeness, which can be related to the difference in spatial/temporal scales between the satellite data and the reference, is also not often taken into account. Further, because the ECVs tend to cover quite long periods of time, the quality and sampling of the reference datasets also changes over time and this evolution of the reference networks should be taken into account when using the validation data to assess a given product, particularly when looking for trends in the data.
Recommendation 5-Traceable Assessment of Level-1 Data All satellite-derived ECV products start by using Level-1 data  [26] , and for passive sensors it generally consists of geolocated and calibrated radiances, while other measured quantities (apart from radiances) will be used for active sensors. All sensors need to have some form of calibration (on board and/or post-launch) to derive the required measurand at Level-1. It is often assumed that it is the responsibility of the Level-1 provider to ensure that the data is as well characterised as possible and that the data can be efficiently used without modification. However, there are cases where the assumption of a reliable Level-1 data set has been shown to be wrong within the lifetime of a given sensor series and where operational modifications and/or external recalibrations must be undertaken to reduce the calibration error.
Recommendation 7-Retrieval Algorithm Round-Robin Comparisons It is vitally important that the retrieval methodologies applied are optimal given the data being used. It has become apparent, however, that there can still be a range of different algorithms used by different groups to derive climate data even when the input data is the same. For example there are at least four different SST products available from the Group for High Resolution Sea Surface Temperature project (GHRSST,  [34] ) which are based on identical Level-1 inputs from the time-series of AVHRR but which all have different validation statistics. Figure  5  shows the median bias and robust standard deviation (both the median and robust standard deviation are robust to outliers e. g. ,  [35] ) for 12 months of SST data from the four SST datasets observed in 2014.
Recommendation 8-Quality of all Ancillary Input Data Many products use ancillary data and or models as part of their retrieval scheme. These data range from climatological datasets, Numerical Weather Prediction (NWP) modelled data, models of surface properties, to models of aerosol. Different data producers will have made different choices regarding which models to use and sometimes the ancillary data used can be very old, likely due to code heritage reasons. There is therefore a need for data providers to justify the use of all ancillary data and/or model inputs relative to the latest knowledge about any given process. A number of problems with some of the models have been captured during the product evaluation process. For example, three examples are highlighted.
1. For the ocean retrieval of aerosol a whitecap fraction model is used which is very old and probably should be updated  [36]. There are much more recent models available and it has been shown that the Monahan model will lead to biases being introduced  [37]. 2. For the Soil Moisture CCI passive retrieval, an old model  [38]  is used. More modern models have been shown to outperform this model, so an update should be implemented; and 3. Many processes use climatological data as input to their retrieval. Care needs to be taken that the optimum data is used. For example, the CCI/C3S Aerosol product uses a Chlorophyll concentration climatology based on Coastal Zone Color Scanner Experiment (CZCS) data (a very old instrument) where there is almost certainly better data available.
Recommendation 9-Consistent Quality Flags Between products for the same ECV as well as across ECVs themselves, there is little consistency between the implementation of quality flags. Quality flags are very useful for the user and ideally should be easy to use and interpret allowing data filtering and enhancing knowledge of the production issues as well as pixel level uncertainties. Evaluation of the demonstrator ECV products revealed that data providers define quality flags differently, making comparisons between datasets difficult. Even in the case where the quality flags have been formally defined to be present across a range of products as is the case with SST, the actual use and meaning of different quality flags varies and can still vary from product to product. Recommendations on an initial set of data product quality flags that should be implemented widely have been provided in  [3]  and consist of the following: number of observations used in the calculation; snow/cloud cover; back-up algorithm implementation; fill-values utilised; pixel-based uncertainty estimates.
Recommendation 10-Cloud Masks and Classification Routines There are often times when some sort of classification is required to retrieve the correct parameter. Probably the most common of these are cloud masks but also includes classification of surface properties (i. e. , land cover classes) and/or classification of parameter type such as aerosol type. Getting the classification wrong or using different interpolation methods to grid data can lead to significant biases in the final data. For example, the technique applied to transform a network of point in situ measurements to a set of gridded values may be greatly affected by the density of observations available. A comparison between two in situ derived gridded datasets-CRU and Global Precipitation Climatology Centre (GPCC)  [39] -demonstrates that differences are slight for grid cells with many measurement stations in proximity.
For cells where such stations are sparse however, anomalies at individual stations cause greater differences between the datasets. Cloud masks are used in many ECV products, but often bespoke schemes are employed so it is very difficult to compare products. Most of the cloud masks used in the ECVs evaluated seem to be based on threshold based tests where a pixel is flagged as cloudy if it passes (or fails) a series of threshold based tests. This cloud masking technique has a long heritage and some cloud masking routines can have dozens of different threshold tests. One advantage of a threshold test is that the individual tests can focus on potentially problematic cloud types which may allow a more certain detection of specific clouds can be hard to find. However, the key problem with using a threshold based system is that it is harder to take uncertainty into account since the thresholds are generally pass/fail and dataset noise is not considered.
Summary and Future Recommendations Here we have presented an initial framework for the Evaluation and Quality Control of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and comprehensive evaluation of a selection of 24 individual satellite and in situ observation derived products across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. An EQC CMS has been developed to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users.
Implementation Each individual data product to be catalogued within the CDS will require: 1. A QAT to be completed; 2. An independent assessment of the QAT information; as well as 3. A CDS placeholder for the dataset. The CMS will need to be expanded to handle these features for all the data types including observations, model-based climate reanalyses, seasonal forecast data products, and climate model simulations including projections. Data type specific QATs will need to be developed along with relevant evaluation questions, assessment process and publishable QARs. Enhancing the CMS functionality in relation to data import, auto-save and account synchronisation will ensure a seamless integration of these additional templates and processes into the CDS. When implementing the EQC functionality for the multitude of data products to be hosted through the CDS, it will be necessary to address several aspects such as the minimum requirements of QAR content before a data product can be listed in the CDS.
Additional Functionalities Additional useful functionalities of the EQC CMS may include: development of a QAR comparison tool to enable direct comparison of similar ECV data products; and the ability to track changes (time and date stamped) in the QAT throughout the review process to ensure both the product producer and expert reviewer are using the most current version of the template. Finally, it is recommended that the EQC invest in the development of a tool that is capable of making detailed and interactive product traceability chains to augment the product generation section of the QARs.
Acknowledgments: The authors wish to acknowledge colleagues who were former representatives of the  University of Reading  ( Laura Carrea  and  Margaret Woodage ) and  Telespazio France  ( Caroline Quod  and  Christelle Barbey ), who contributed to the development of the EQC for observations. We could also like to acknowledge the individual product producers who worked with us to complete QARs for the 24 demonstrator products. As well as  Alex Hale  from  NPL  for project management support.
RNA POLII WT elongation as treatment with flavopiridol, a specific inhibitor of the positive transcription elongation factor P-TEFb  [30] , resulted in similar fold decrease of nascent transcripts in WT and Trim33 -/-BMDM (Fig. 3c ). Antisense promoter transcription, a mark of high transcriptional activity  [31] , was increased in Trim33 -/-BMDM (Fig. 3d ) and integration of three chromatin datasets by ChromHMM in Atp1b3 gene body showed broader H3K4me3 and H3K79me2 but not H3K36me3 regions in Trim33 -/-BMDM compared to WT BMDM (Fig. 3e ) indicating that increased transcription of the Atp1b3 gene in Trim33 -/-BMDM was mainly accounted for by enhanced transcription initiation rather than transcription elongation. Similar results were found for the Rnf7 gene (Additional file 3: Figure  S3c ).
Chromatin Immunoprecipitation (ChIP) and ChIP-seq analyses ChIP experiments were performed essentially as previously described  [9]. For ChIP-seq, libraries were prepared with the MicroPlex Library Preparation kit v2 (Diagenode) and sequenced on Illumina Hiseq 4000 following Illumina's instructions. Single-end 50 bp reads were mapped to the mm9 genome assembly using Bowtie 1. 0. 0 with the following arguments: -m 1 -strata -best -y -S -l 40 -p 2. Peak detection was performed using the MACS software  [48]  with the corresponding input as control sample. Regions were considered significantly enriched at a p value threshold of 1e-05. Peaks were then annotated according to genomic features of Ensembl v67 using Homer (http://homer. salk. edu/homer /). Promoter-TSS was defined from -1 kb to + 100 bp relative to the position of the TSS and TTS from -100 bp to + 1 kb relative to the position of the TTS.
Intergenic SPT16 peaks that overlap with the mm9 blacklist from ENCODE were additional filtered out. Functional annotation analyses were performed using GREAT or DAVID web-tools. Overlap between SPT16 and PU. 1 or TRIM33 peaks was performed using BedTools, requiring at least 1 bp overlap. Enrichment profiles and comparisons of ChIPseq datasets at indicated regions were performed using DeepTools on Galaxy (https ://usega laxy. org/). Data were represented as heatmaps, average density plots or boxplots. Annotation of combinatorial chromatin states for H3K4me3, H3K79me2 and H3K36me3 marks was performed with the ChromHMM software (http://compb io. mit. edu/Chrom HMM/) at 200 bp bin interval in WT and Trim33 -/-BMDM using the corresponding input as background control.
Acknowledgements The authors thank  S. Le Gras  and  B. Jost  for excellent support in bioinformatic analyses and the IGBMC high-throughput sequencing facility, a member of the " France Génomique "  consortium  ( ANR-10-INBS-0009 );  F. Le Naour  for mass spectrometry analysis and  C. Carles ,  N. Gault ,  S. Moreno ,  G. Rousselet  and  D. Lewandowski  for critical revision of the manuscript.
Availability of data and materials The datasets generated during the current study are available in the Gene Expression Omnibus (GEO) repository under accession number GSE132924. H3K27ac and H3K4me1 ChIP-seq datasets were retrieved from ENCODE, MNase-seq from GSE50762, PU. 1 ChIP-seq from GSE19553, H3K4me3 and TRIM33 ChIP-seq datasets from GSE43654.