dataesr/scientific-paragraphs-categorization huggingface dataset:
The different numbers among different satellites are mainly due to different period of satellite data. The differences between E1 and E3 are mainly due to the different coverage of clouds and other factors such as the effects of the sun glint. As these satellites have run more than ten years, it indicates that one satellite measures Chl-a only about one time during a period of ten years at a fixed pixel. This result is consistent with the spatial coverage of valid pixels for one satellite (about 14%). However, the daily climatology offers a gap-free coverage of the Chl-a dataset. Comparing the patterns of climatology to satellite data in Figure  10 , the climatology is around the centers of dots of satellite-retrieved Chl-a according to the day of year. The dot patterns of the satellite-retrieved are much more scattered than that of the climatology.
It demonstrates that large MRD values are caused by the temporal variability of satellite-retrieved Chl-a of different years over the coastal and frontal zones. The mean MRD values of the images are 16. 6%, 15. 9%, 20. 8%, 16. 8%, 17. 0%, and 17. 4% for SeaWiFS, MODIST, MERIS, MODISA, VIIRS, and merged data, respectively. The value of MERIS is the largest due to its spatial/temporal characteristics of images being substantially different from the other four satellites. However, these results cannot be used to evaluate the actual accuracy of each satellite because we take the fitted values of Equation (3) as the "truth value", which is actually the average of satellite data. As MERIS takes only a small part (about 15%) of the dataset and other satellites are similar, it may cause the patterns of MERIS in Figure  11  to differ from the others.
It also causes the MRD of merged data less than that of MERIS but larger than the others. We also use Equation (  2 ) to compute MRD values with similar image patterns of Figure  11. The mean MRD values are 16. 8% (SeaWiFS), 16. 0% (MODIST), 20. 9% (MERIS), 16. 9% (MODISA), 17. 2% (VIIRS), and 17. 5% (Merged), respectively. Compared to the MRD value of the daily climatology (3. 3%), the climatology can significantly reduce the MRD values of satellite data.
The Timing of Phytoplankton Blooms Phytoplankton blooms are a recurrent feature each year and play an important role in oceanic uptake of atmospheric carbon dioxide and fishery stocks  [37, 44]. The phytoplankton bloom can be identified by the maxima of Chl-a  [45]. In fact, there are many abnormal values in satellite Chl-a, easily obtaining a wrong bloom time identification. When the climatology is fitted by the sine equations, the bloom period can be exactly identified by the biggest peak of the fitted values, as shown in Figure  12a. Remote Sens. 2020, 05, x FOR PEER REVIEW 16 of 22 "truth value", which is actually the average of satellite data. As MERIS takes only a small part (about 15%) of the dataset and other satellites are similar, it may cause the patterns of MERIS in Figure  11  to differ from the others.
The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high precision scientific data on the carbon cycle and the greenhouse gas (GHG) budget. All stations have to undergo a rigorous assessment before being labeled, i. e. receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmospheric network through the 23 stations that have been labeled between November 2017 and November 2019. We describe the labeling steps as well as the quality controls used to verify that the ICOS data (CO 2 , 5 CH 4 , CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the GHG data, three to four calibration gases and two target gases, one measured two to three times a day, the other with the calibration gases (twice a month) are measured.
WMO calibration scales), the Central Radiocarbon Laboratory (CAL-CRL) for radiocarbon analysis, next to three thematic centers for atmosphere, ecosystem and oceans. The thematic centers are responsible for data processing, instrument testing and developing protocols in collaboration with station Principal Investigators (PI's). Regular Monitoring Station Assembly meetings (MSA) facilitate discussion of technical and scientific matters. The Atmospheric Thematic Center (ATC, https://icos-atc. lsce. ipsl. fr/) based in France and Finland, is divided into three components; the metrology laboratory (MLab) responsible for instrument evaluation, protocol definition and PI support, the data center responsible for data processing, code development and graphical tools for PI's, and finally the MobileLab in Finland, tasked with the audit of the stations during and after the labeling process.
One very important task for ATC is ensuring that the stations reach the quality objectives defined within ICOS, based on the compatibility goals of the  WMO (WMO, 2018)  and detailed in  ATC and Laurent (2017). To do so, a so-called "labeling process" has been developed to firstly assess the relevance of a new measurement site, as well as the adequacy of the human and logistical resources available with the ICOS requirements. Downstream, an evaluation of the first months of measurement is carried out, verifying compliance with the ICOS protocols. The Carbon Portal (https://www. icos-cp. eu/), which is responsible for storage and dissemination of data and elaborated products is associated in the labeling process via PID/DOI attribution for the data and the provision of a web interface to gather important information needed for the labeling.
Protocol and metrics of the labeling process To be labeled, an ICOS atmosphere station has to follow the guidelines and requirements defined in the ICOS Atmospheric Station specifications  (ATC and Laurent, 2017)  and the labeling document (available on https://icos-atc. lsce. ipsl. fr/node/99/ 27246). The specifications are discussed and updated if necessary every six months at the Monitoring Station Assembly meetings that include the PI's of all the ICOS stations and representatives of the central facilities. The goal of these specifications is to allow each site to reach the performances required by the ICOS atmosphere data quality objectives, which are principally adhering to the WMO guidelines like  WMO (2018)  for the greenhouse gas observations but are more elaborated in  ATC and Laurent (2017)  and presented in section 2.
4 below. The labeling process of atmospheric stations has been defined as a three-step process: Step 1: evaluation of the station location and infrastructure, Step 2: station performances, Step 3: official and formal ICOS data labeling by the ICOS General Assembly (composed of representatives of the Member and Observer countries of ICOS and meeting twice a year). To pass Step 1, stations must submit information about the infrastructure of their site, its location and its proximity to anthropogenic sources like cities or main roads. This is done through the Carbon Portal interface (https://meta. icos-cp. eu/ labeling). The ATC uses these data to compile a report and issue recommendations to the ICOS Head Office who will then approve or reject the application. Usually, if there are some problematic points, the ATC exchanges first with the PI to see if improvements can be done to meet the requirements or ask for additional documents.
ICOS atmosphere is mainly focused on tall tower sites, measuring regional signals but accepts in a limited number high altitude and coastal sites  (ATC and Laurent, 2017). Once Step 1 is approved, the station can be built, equipped and set up to fulfill the ICOS Atmospheric Station Specifications. Once the near-real time data flow to the ATC database is established  (Hazan et al. , 2016) , stations can apply for Step 2. The time lap between Step 1 and Step 2 can vary greatly depending on the sites. Indeed, in case of already existing stations, they are entering ICOS with already running instruments and historical datasets and need only small changes such as getting the calibration cylinders from the CAL-FCL and modifying some procedures to have their data processed into the database before beginning Step 2.
During Step 2, a phase of measurement optimization begins; the initial test period. This is done in close collaboration between the station PI and the ATC thanks to routine sessions of data evaluation. This period typically lasts 4 to 6 months to gather data to evaluate their quality. The period may be prolonged if needed. If data meeting the specifications are available prior to the Step 2 application, the initial test period can be shorter. During the initial test period, the requirements detailed hereafter are asked from the station PI in order to be able to analyze all the data in a uniform way for all sites.
General requirements At the beginning of the initial test period, a station must provide at minimum continuous in-situ greenhouse gas data to the database on a daily basis, and by the end, meteorological parameters (wind speed, wind direction, atmospheric temperature, relative humidity and pressure) and additional diagnostic data (room temperature, instrument and flushing pump flow rates). Table  1  shows the list of all mandatory parameters that should be provided by the stations depending on the station class. Furthermore, it also provides a list of recommended parameters. A Class 1 station will provide more parameters than Class 2, but both stations must meet the same level of data quality. Presently, the MSA has decided that labeling should not be contingent upon two Class 1 parameters: the boundary layer height and greenhouse gases and 14 C values from flask sampling.
Indeed, for these two parameters, the technologies, hardware and software are still in development or in need of improvement  (Feist et al. , 2015; Levin et al. , 2020; Poltera et al. , 2017). As soon as the MSA decides to approve a technology, it should however be added to the station as soon as possible. Indeed, flask sampling is an additional quality control tool as well as a way to sample species that cannot be yet measured continuously while boundary layer heights would help improve models. For all the stations presented here, we focused on CO 2 and CH 4 continuous measurements for all sites and on CO measurements for class 1 sites. The other species measured by some instruments such as N 2 O were not assessed as not mandatory. The instruments providing the data must be ICOS compliant as defined in the specification document.
Manual data quality control All the data processed by ICOS ATC are going through an automatic quality control (QC) based on different criteria  (Hazan et al. , 2016). However, as a second and final quality control step, the PI of the station has to control and validate the data on a regular basis using the logbook information from the station (e. g. contamination due to a maintenance on site). No data is flagged invalid without an objective reason. To harmonize the quality control, the ATC provides dedicated software tools and organizes mandatory training that must be attended before beginning the operation at the station. The data validation is done with a software developed at the ATC directly on the server. On a daily basis, the station PI's check the ATC data products generated daily as an early detection of any issue related to the analyzers, sampling lines, data transmission or processing.
On a weekly basis, raw greenhouse gas data have to be checked and (in)validated using ATC-QC software via a flagging scheme. On a monthly basis hourly average of greenhouse gas and meteorological data must also be controlled. During the initial test period, regular online meetings take place with the PI's and the ATC to review the data and assist the PI's. Their purpose are: to exchange expertise between ATC and the PI's, for example on how to QC local events (spikes) and how to interpret the data products. For example, for the spikes, a spike detection algorithm has been developed and is automatically applied on the data  (El Yazidi et al. , 2018). to make sure the data are regularly controlled. to benefit from local knowledge to explain patterns detected in the time series.
Percentage of data validated by station PI's Quality control by the PI's is paramount to ensure the high quality of the ICOS dataset. When preparing the reports, we make sure that the quality control is done as detailed in the ICOS specifications, i. e weekly for the greenhouse gas air raw data and monthly to bi-monthly for the hourly means/injections of greenhouse gas (air and quality control gases) and meteorological data. Figure  3  shows the status of hourly data validation at a given time for six stations. Data are considered valid when green, and invalid when red. Dark colors indicate automatic (by the ATC software) validation, prior to the manual data inspection by the PI (light colors). Dark red color will usually indicate flushing periods or instrument failure (hence no data while the database expects some) that are automatically flagged.
For each station, the analyzers are identified by their unique ID attributed by the ATC. This is the number shown in the second column in the graph. The third column shows the sampling heights. In this plot, however, we will mostly focus on the amount of manual validation to ensure that the data have been indeed controlled by the PI's. All intervention of the PI's to flag data are done through the ATC software and are recorded for traceability. When raw data are rejected, the PI has to select a reason for the problem within a predefined list of issues (such as Flushing period, Instrument failure, Maintenance,. ). Minute and hourly averages are automatically reprocessed after any invalidation of raw data.
Percentage of air measurement vs calibrations/target and flushing time When switching from one sample to the other, there is some flushing period (defined by the PI in agreement with the ATC) that is automatically removed from the valid data. Figure  4  allows evaluating that the time spent measuring "invalid" air is not too high compared to the time measuring "valid" air. It is mostly important for tall towers that switch between many levels and may end up spending too much time flushing. On the figure, when the percentage does not reach 100%, it means that the station has not yet provided data for the whole year. For example at Lindenberg, the analyzer for CO 2 and CH 4 was at least.
Temperature dependence of the instruments A few of the instruments tested at the ATC MLab have shown a significant sensitivity of the GHG measurements to temperature. On site, the temperature variation is supposed to be small but in case of problem with the air conditioning, we can use the target https://doi. org/10. 5194/amt-2020-213 Preprint. Discussion started: 15 July 2020 c Author(s) 2020. CC BY 4. 0 License. gas measurement to evaluate the impact of the temperature changes on the measurements as seen in Figure  8. Currently, there is no correction derived from the MLab tests applied in the ICOS data processing. For instruments showing a significant variability due to the temperature or other parameters (i. e. leading to exceed the WMO goals within the observed range of temperature), it is recommended to use of an additional gas tank, so called short term working standard, in order to correct the short term variability induced by these sensitivities.
Meteorological measurements Meteorological parameters are mandatory as they are used to analyze the atmospheric signals measured at the station location, and associate them to regional or large scale processes. During the initial test period, the ATC checks that the sensors are compliant and the data are transmitted correctly to the database for all mandatory levels (see Figure  9 ). The ATC checks the data availability and consistency. If meteorological data are sent to the database at the same time as the greenhouse gas data and not at the end of the test period, they can be used to understand the variability in the greenhouse gas data.
Presentation of the 2labeled stations The 23 labeled stations described here passed Step 1 between 2016 and 2019 (see Table  3 ). For most of them, this was a straightforward step. For four of them (Ispra, Observatoire de l'Atmosphère du Maïdo, Lutjewad and Karlsruhe), additional documents and preliminary studies were requested mainly to address potential local contaminations. After Step 1 approval, they entered Step 2 up to two-and-a-half years later depending on the existing infrastructure and instrumentation. At the end of the initial test period, a scientific report summing up the setup done during this period and the resulting data was sent to the PI and the station was proposed for labeling. The stations were then approved by the ICOS General Assembly. In November 2017, the first four ICOS atmosphere stations were labeled following this procedure.
In May 2018, the next seven were approved. In November 2018, another six were labeled. In May and November 2019, two and four respectively were approved. Seven stations are located in Germany, three in mainland France, three in Sweden, three in Finland, two in Italy The 23 stations cover the majority of western Europe with the most southerly in Italy and the furthest north on Svalbard in the Arctic Circle plus one located in the Indian Ocean in the Southern Hemisphere (see Figure  13  and Table  3 ). The first ICOS compliant data date from the end of 2015 for two German stations (who began measurements following ICOS procedure before the operational phase and so the Step 1 application) and the more recent stations have compliant data since September 2019. Fifteen stations are continental sites equipped with tall towers with up to six air sampling levels.
Uncertainties Figures 16, 17 and 18 show the uncertainties and bias of the short-term target for CO 2 , CH 4 and CO as defined in Figure  13. They are calculated using one year of data. For each station, we show two boxes and a dot. The left box (in pink when large enough to see) uses data from the year before the date of the end of the initial test period. The right box (in blue when large enough to see) uses the last year from March 2019 to March 2020. The dot shows the MLab initial test values. For example, for a site labeled in May 2018, we use data from April 15 th 2017 to April 15 th 2018. For the sites labeled last, the periods are almost the same. For some sites, instruments have changed since the labeling and thus there will be four boxes. For all species, the CMR and LTR are close to the values calculated at the Mlab and the bias for all sites is mostly within the WMO recommendations.
ICOS ATC recommends the use of single tube Nafion instead. Another problem was detected at Hyltemossa that potentially affected Norunda and Svartberget as the three stations were using the same system on their tall towers. The lower sampling level with a low flow rate showed a positive bias in CO compared to the other sampling levels that could not be explained by the origin of air masses. The intakes in the tower were heated to avoid condensation or ice. This bias was caused by those heated intake cups. It is unclear whether the excess CO originated from the heater or from a plastic part. To be on the safe side, the heating was turned off and the plastic parts replaced by a Teflon ones. After these modifications, no CO bias was observed anymore. About four months of data were invalid for this lower level at HTM as a result.
Conclusions In this paper we have presented the process conducting to the labeling of an ICOS atmospheric station. This process ensures that the produced data are of high-quality. For the 23 labeled sites, the PI's implemented the specifications and completed training at ATC before beginning the initial test phase or just after. This allowed for high-quality data with only few problems detected. The problems were handled and solutions were implemented to get good quality data. Other stations would benefit from the lessons learned during the certification process. From these data, we can conclude on different topics: regular calibrations (twice a month) are important in particular to follow the CRDS analyzer's CO variability. reducing the number of calibration injections (from four to three) in a calibration sequence is possible and allows to save gas, extend the calibration gas lifespan and maximize atmospheric measurement time.
Acknowledgements. The French monitoring network acknowledges the long term support received as part of the  Service National d'Observation program  and thanks its technical staff. I. M. and J. L. thanks the SMR technical staff and funding from  ICOS-Finland  ( 281255 ). This work was 43 https://doi. org/10. 5194/amt-2020-213Preprint. Discussion started: 15 July 2020 c Author(s) 2020. CC BY 4. 0 License. supported by the  Ministry of Education, Youth and Sports of CR  within the  CzeCOS program , grant number  LM201812. ICOS-Switzerland  is funded by the  Swiss National Science Foundation (Phase I  ( 2013-2017 ):  20FI21_148992 ;  Phase II  ( 2017-2021 ):  20FI20_173691 ) and in-house contributions. ICOS Netherlands is substantially supported by the  Dutch Research Council NWO  through the  Ruisdael large scale infrastructure  project.
Acknowledgements The authors thank  Dr. H. Arjmandi Tash  for his experimental contributions in the preliminary phase of the project. This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  grant agreement number  335879  project acronym " Biographene " and the  Netherlands Organisation for Scientific Research  ( NWO-VIDI 723. 013. 007 ).
Microscopy image analysis 3D FISH analysis was conducted using the "Image Processing Toolbox" in MATLAB (version R2018a). Briefly, the channels were smoothed using 3D Gaussian filters (Σ = 3 and 1 for DAPI and FISH channels, respectively) and segmented in 3D; FISH objects that were smaller than 50 voxels or located outside of the segmented DAPI were discarded. Last, 3D distances between the centroids of FISH objects were calculated, and only the mutual nearest neighbors that were closer than 0. 8 m were considered. For measuring the area of DAPI-and green fluorescent protein (GFP)-positive cells in eye discs, images of individual discs were smoothed using Gaussian filter (Σ = 6) and segmented using Otsu's method. Holes within segmented discs were filled using the imfill function before area measurement.
For the visualization and the quantification of ChIP-seq tracks, reads per kilo base per million mapped reads (RPKM)-normalized bigWig binary files were generated using the bamCoverage function from Deeptools2 (v2. 5. 1)  (34)  with the following parameters: -of = bigwig --samFlagExclude 128 --ignoreDuplicates -e 200 -normalizeUsingRPKM. Last, replicates were merged using the bigWigMerge and bedGraphToBigWig tools from the University of California, Santa Cruz  (35) , with default parameters. For the visualization of the tracks, the Integrative Genomics Viewer  (36)  and the "seqplots" R package (v1. 20. 0) (37) were used.
Genome-wide clustering of PTM ChIP-seq signals from ED using SOMs SOMs have been previously shown to be a powerful method to identify complex relationships between genomic data  (9). This algorithm relies on a machine learning-based process that trains for a given number of iterations to build a model, which is used to produce intuitively readable maps. Average ChIP-seq enrichments were computed genome wide (2. 5-kb bins) for H3K4me3, H3K4me2, H3K36me3, H3K27Ac, H4K20me1, H3K4me1, H3K27me1, H3K27me3, H2AK118Ub, and H3K27me2 in ED using bwtools  (38). The resulting matrix was scaled and used to train an 80 × 80 toroidal SOM for 100 iterations using the "kohonen" R package (v3. 0. 7)  (39, 40). Then, we overimposed a k-means clustering directly on the SOM nodes using the "kmeans" function in R (R version 3.