dataesr/scientific-paragraphs-categorization huggingface dataset:
European Station for Time series in the Ocean, Canary Islands (ESTOC) ESTOC is an open-ocean monitoring site located in the eastern North Atlantic subtropical gyre. ESTOC was initiated in 1991 with particle flux measurements, and in 1994 began standard observations of the water column, in addition to the deployment of a current meter mooring. The core parameters measured at ESTOC include salinity, temperature, current speed, nutrients, chlorophyll, inorganic carbon, particulate organic carbon and nitrogen, and sinking particle flux  (Neuer et al. , 2007). For this work, measurements of chlorophyll-a concentration from monthly cruises from 1994 to 2011 were used. These data were provided to CCI following a specific request. The time of day was unavailable and was set to 12:00:00 (UTC). These observations were flagged with "1" in column "flag_time".
2. 2. 22 One is a data collection entitled "IMOS National Reference Station (NRS) -Phytoplankton HPLC Pigment Composition Analysis", which was acquired from the Australian Ocean Data Network portal (https://portal. aodn. org. au, last access: 18 December 2022). This data set comprises of phytoplankton pigment composition measured by HPLC collected with small vessels on monthly basis at nine National Reference Stations as part of the IMOS National Mooring Network. The other chlorophyll-a data set measured by HPLC and fluorometry methods is a subset (2015-2021) of the IMOS Bio-optical Database also available through the AODN portal. This database comprises of a suite of bio-optical parameters from samples collected during research voyages in Australian waters and is used by the IMOS Ocean Colour Sub-Facility to assess the accuracy of satellite ocean colour products in Australian coastal and open ocean waters  (Schroeder et al.
MARineEcosytem DATa (MAREDAT) MAREDAT database is a global assemblage of pigments measured by HPLC  (Peloquin et al. , 2013a)  from combination of 136 independent field data sets, solicited from investigators and databases. The database provides high quality measurements of taxonomic pigments including chlorophyll a and b, 19'-butanoyloxyfucoxanthin, 19'hexanoyloxyfucoxanthin, alloxanthin, divinyl chlorophyll-a, fucoxanthin, lutein, peridinin, prasinoxanthin, violaxanthin, and zeaxanthin. The database is available through PAN-GAEA (https://doi. org/10. 1594/PANGAEA. 793246,  Peloquin et al. , 2013b). For this work, only measurements of total chlorophyll-a flagged with high quality were used. The time of day was unavailable and was set to 12:00:00 (UTC). These observations were flagged with "1" in column "flag_time".
Palmer Station Long-term Ecological Research (PALMER) PALMER is a monitoring station located in western Antarctic Peninsula. The Palmer station investigates the marine ecology of the Southern Ocean with focus on the pelagic marine ecosystem, including sea ice habitats, regional oceanography, and nesting sites of seabird predators. The PALMER data include measurements of meteorological, oceanographic, sea ice, predators, nutrients and biogeochemistry, pigments, primary production, zooplankton, and microbes parameters. This work used the   tica LTER et al. , 2018 tica LTER et al. , , 2020c)). The compiled variables were "chla_hplc", "chla_fluor".
SeaDataNet archive (SEADATANET) SeaDataNet is a Pan-European infrastructure for ocean and marine data management. It aims to develop a standardized system for managing large and diverse data sets collected by oceanographic cruises and automatic observation systems. For this work, discrete chlorophyll-a concentration observations with an "access restriction" set to "academic" and "unrestricted" were acquired from the SeaDataNet platform with guidance from the helpdesk. Only data from the "Institute of Marine Research -Norwegian Marine Data Centre (NMD), Norway", which comprised most of the acquired data, were used. All chlorophyll observations were from discrete samples measured by fluorometric, spectrophotometric, or HPLC methods, but the exact method was not given. Thus, the observations were marked as "chla_fluor", although some were possibly from HPLC measurements, and were flagged with "1" in a column "flag_chla_method".
Data provided by Trevor Platt and Shubha Sathyendranath (TPSS) In this work, the TPSS data source refers to a group of observations that were provided to this compilation by Trevor  Platt and Shubha Sathyendranath. This is a collection of biooptical in situ data collected during cruises predominantly in the Northwest Atlantic, but also from the Indian Ocean, South Pacific, and Central Atlantic (see  Sathyendranath et al. , 2009  for additional details regarding the cruises). It comprises measurements of phytoplankton pigments and algal pigment absorption coefficients. The time of day was unavailable and was set to 12:00:00 (UTC). These observations were flagged with "1" in column "flag_time". The compiled variables were "chla_hplc", "chla_fluor", and "aph".
Bio-optical data from Tara expeditions (TARA) The Tara expeditions consist of several cruises around the world, some with durations of several years, designed to study and understand the distribution of planktonic organisms in the world ocean. The discrete observations of remote sensing reflectance and chlorophyll-a concentration from HPLC measurements taken during the Tara "Oceans" (2009-2013) and "Mediterranean" (2014) expeditions were considered in this work. These data were provided to ESA OC-CCI project by Emmanuel Boss and were available in the SeaBASS archive. The remote sensing reflectances were corrected for the bidirectional nature of the light field (Morel  All chlorophyll data were considered, but for a given station, HPLC data were selected if available. Crosses show sites from where data of chlorophyll are available in a specific geographic location.
Results In this work, several sets of bio-optical in situ data were acquired, homogenized, and merged into a single unified data set. The data set comprises in situ observations between 1997 and 2021, with a global distribution, and includes the following variables: "rrs", "chla", "aph", "adg", "bbp", "kd", and "tsm". All observations were processed in such a way that they can be compared directly with satellite-derived ocean colour data. The compiled data set corresponds to a table with a total of 151 673 rows and 3458 columns. Each row represents a unique station in space and time, separated from the rest by at least 5 min and 200 m. For each variable at a given station, three metadata strings are provided: "dataset", "subdataset", and "contributor". The columns of the table take the form described in Table  1.
Furthermore, duplicates across contributing data sets were removed (e. g. NO-MAD and others, such as MOBY, were removed from MER-MAID; also, data of individual projects, such as PALMER and AMT, can be listed under NOMAD). Nevertheless, the reported numbers still give a general view of the contributions from each data set and provide users with valuable information for analysing each set of data separately. Observations of remote-sensing reflectance are available at 948 unique wavelengths (i. e. columns), between 313 and 1022. 1 nm (Fig. 1 ). In total, there are 68 641 observations (i. e. rows) of remote-sensing reflectance. The total number of observations are partitioned per contribut-ing data sets as follows: AERONET-OC (34 551), BOUS-SOLE (22 620), MOBY (6034), NOMAD (3326), MER-MAID (895), SeaBASS (730), AWI (71), COASTCOLOUR (307), and TARA (107).
Data from AERONET-OC, BOUS-SOLE, and MOBY correspond to continuous time series, and hence the higher number of observations. In comparison with the previous version  (Valente et al. , 2019) , which had reflectance data until 2018, the number of stations increased by ∼ 15 % (i. e. from 59 781 to 68 641). The new data points   ([443,490,510] /555 or  [443, 490, 510 ]/560 if 555 not available) as a function of chlorophyll-a concentration. All chlorophyll data were considered, but for a given station, HPLC data were selected if available. Data within 2 nm of the wavelengths were used. For reference, the solid and dotted lines show the NASA OC4 and OC4E v6 standard algorithms, respectively (https://oceancolor. gsfc. nasa. gov/atbd/chlor_a/, last access: 18 December 2022). The total number of points was 3645, of which 80 % were from NOMAD.
are mainly from recent years (2019-2021) and from updates of AERONET-OC, BOUSSOLE, MOBY, MERMAID, and AWI. The new data extended the temporal coverage towards more recent years, but the statistical distribution of values and the spatial coverages (discussed below) have essentially remained the same when compared to the previous version  (Valente et al. , 2019). This is explained by most of the new observations coming from continuous time series at fixed the locations (AERONET-OC, BOUSSOLE, MOBY). The distribution of the remote sensing reflectances at 44X nm and 55X nm is provided in Fig. 2a  and b , respectively. Data were first searched at 445 and 555 nm, and then with a search window up to 8 nm, to include also data at 547 nm. Median values at 44X nm ranged from 0. 003 m -1 (AERONET-OC) and 0.
Away from continental margins, the Atlantic Ocean has the highest density of observations. Best geographic coverage is provided by the NO-MAD database. Data from SeaBASS is also well dispersed in space but fewer in number. Data from MERMAID are mainly located along the coasts of Europe, North America, and the central region of the North Atlantic Ocean. The observations from AERONET-OC, BOUSSOLE, COASTCOLOUR, and MOBY are concentrated in specific sites around the world, while AWI data are available for the Atlantic and Arctic Oceans. TARA data are spread across several regions, with highest data density in the Mediterranean Sea. Observations of chlorophyll-a concentration were divided into those measured by fluorometric or spectrophotometric methods ("chla_fluor"), and HPLC methods ("chla_hplc").
For additional analysis and as an example of the applications of the compiled dataset, the combined chlorophyll data ("chla_fluor" and "chla_hplc") were partitioned into 5 • × 5 • boxes, and for each box the number of observations, average value, and standard deviation were computed (Fig. 10a, b,  and c , respectively). The number of observations can be very high (> 1000) in some boxes along the European and North American coastlines and relatively low (< 20) in oceanic regions. The well-known global biogeographical features, such as the lower chlorophyll in the subtropical gyres and higher values in coastal and upwelling areas, clearly emerge in the average value map (Fig. 10b ). There is a close correspondence between the spatial patterns of the average and standard de-viation maps (Fig.
The distribution of the ratios is shown in Fig. 13. Several observations were found to be outside the thresholds used in the International Ocean-Colour Coordinating Group (IOCCG) report 5 for quality control  (IOCCG, 2006 ; see dotted vertical black lines in Fig. 13 ). These points are highlighted here for information but retained in the database, since these were mostly from NOMAD and there was an in-Table  3. Summary of median values for "aph", "adg", and "bbp" at 44X and 55X nm for each data set (as shown in Fig. 12a-f ). Data were first searched at 445 and 555 nm, and then with a search window up to 8 nm, to include data at 547 nm. terest to preserve this data set as a whole. Also, not discarding these data allows further manipulation with different quality control criteria. On the annual scale, the observations of the inherent optical properties continue to be strongly underrepresented in the Southern Hemisphere where there is a complete absence of data during the austral winter (Fig.
Acknowledgements. This paper acknowledges funding from the  ESA   OC-CCI  project (grant number  4000101437/10/I-LG ), the  EU-METSAT "Multi-mission Ocean Colour Algorithm Prototyping" (OMAPS) , and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  810139 : Project  Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS. This work is also a contribution to project  PEst-OE/MAR/UI0199/2014. We would like to thank the efforts of the teams responsible for collection of the data in the field and of the teams responsible for processing and storing the data in archives, without which this work would not be possible. We thank  Tamoghna Acharyya  at  Plymouth Marine Laboratory  for his initial contribution to this work.
We thank the  NOAA (US)  for making available the MOBY data and  Yong Sung Kim  for the help in questions about MOBY data. BOUSSOLE is supported and funded by the  European Space Agency (ESA) , the  Centre National d'Etudes Spatiales (CNES) , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National des Sciences de l'Univers (INSU) , the  Sorbonne Université (SU) , and the  Institut de la Mer de Villefranche (IMEV). We thank  ACRI-ST ,  ARGANS , and  ESA  for access to the MERMAID Database (http://hermes. acri. fr/mermaid, last access: 18 December 2022). We thank  Annelies Hommersom ,  Pierre Yves Deschamps , and  David Siegel  for allowing the use of MERMAID data for which they are Principal Investigators. The AMT is funded by the  UK Natural Environment Research Council  through its  National Capability Long-term Single Centre Science Programme,   Climate Linked Atlantic Sector Science  (grant number  NE/R015953/1 ).
We also thank  Giuseppe Zibordi  for data quality control and  Brent Holben  and his team from  NASA Goddard Space Flight Centre  for making the data available online. The AWI data set was supported by the captain, crew, and other scientists at the specific German RVs (Meteor, Heincke,  Maria S. Merian , Polarstern, and Sonne) expeditions and funding by the  Helmholtz Association (HGF Innovative Network Funds Phytooptics, Helmholtz Infrastructure Initiative FRAM) , the  European Union  ( Seventh Framework Programme   SHIVA-226224FP7-ENV-2008-1020 1 ), the  BMBF  ( OASIS FK03G0235A  and  AS-TRA FK03G0243A ), the  DFG  (# 268020496-TRR 172  project C03 within the  Transregional Collaborative Research Center Arc-tiC Amplication: Climate Relevant Atmospheric 1023 and Sur-faCe Processes, and Feedback Mechanisms (AC) 3), the collaborative project  OLCI-PFT (ACRI-AWI Offer  # 209-180104 ), and the  ESA   656 S5P+Innovation Theme 7 Ocean Colour (S5POC)  project (no.
We thank ICES database on the marine environment (Copenhagen, Denmark, 2014) for allowing the use of their archived data, and Marilynn Sørensen for the help with questions about the ICES data set. We thank all ICES contributors for their data. We thank Eric Zettler and SEA Education Association. This study includes data collected by the US Marine sity Observation Network (MBON) funded under the US National Ocean Partnership Program through NASA grant NNX14AP62A "National Marine Sanctuaries as Sentinel Sites for a Demonstration Marine Biodiversity Observation Network (MBON)". The CARI-ACO Ocean Time-Series program also provided significant decadelong bio-optical information used in this study. These data were obtained from NOMAD and SeaBASS. We thank NASA, SeaBASS, and the Ocean Biology Processing Group (OBPG) for access to SeaBASS and NOMAD data.
seadatanet. org, last access: 18 December 2022). We thank Emmanuel Boss for the TARA data. Funding for the collection and processing of the TARA data set was provided by NASA Ocean Biology and Biogeochemistry program under grants NNX11AQ14G, NNX09AU43G, NNX13AE58G, and NNX15AC08G to the University of Maine. We would like to honour the memory of Marcel Wernand, Tiffany Moisan, and Trevor Platt, authors who contributed to the previous versions. Financial support. This paper received funding from the ESA OC-CCI project (grant number 4000101437/10/I-LG), the EU-METSAT "Multi-mission Ocean Colour Algorithm Prototyping" (OMAPS), and the European Union's Horizon 2020 research and innovation programme under grant agreement 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS.
Sloyan, Paul Thomson and Tom Trull Bio-optical Database of Australian Waters. Available from the Australian Ocean Data Network (AODN). Com- piled standard variables: "chla_hplc", "chla_ fluor" MAREDAT Quality controlled global compilation of chla HPLC. Ray Barlow, Robert Bidigare, Herve Claustre, Available from PANGAEA. Compiled standard variable: Denise Cummings, Giacomo DiTullio, Chris Galli- "chla_hplc". enne, Ralf Goericke, Patrick Holligan, David Karl, Michael Landry, Michael Lomas, Michael Lucas, Jean-Claude Marty, Walker Smith, Denise Smythe- Wright, Rick Stumpf, Emilio Suarez, Koji Suzuki, Maria Vernet, Simon Wright PALMER "Palmer station Long-term Ecological Research" Oscar Schofield, Raymond Smith, Maria Vernet SEADATANET Global archive of in situ marine data. Available from Not available SEADATANET website.
Compiled standard variable: "chla_fluor". Ralf Goericke CCELTER Cruise data from "California Current Ecosystem Long Term Ecological Research". Available from CCELTER website. Compiled standard variable: "chla_fluor". Ralf Goericke CIMT Sampling from the "Center for Integrated Marine Technology" (California). Available from CIMT website. Compiled standard variable: "chla_fluor". Raphael Kudela COASTCOLOUR Quality controlled compilation of bio-optical data in several coastal sites. Available from PANGAEA. Compiled standard variables: "chla_ fluor", "chla_hplc", "rrs", "aph", "adg", "bbp", "tsm". (Antarctica). Available from PALMER website. Compiled standard variables: "chla_fluor", "chla_hplc".
Variability in neurodegenerative disease progression poses great challenges for the evaluation of potential treatments. Identifying the persons who will experience significant progression in the short term is key for the implementation of trials with smaller sample sizes. We apply here disease course mapping to forecast biomarker progression for individual carriers of the pathological CAG repeat expansions responsible for Huntington disease. We used data from two longitudinal studies (TRACK-HD and TRACK-ON) to synchronize temporal progression of 15 clinical and imaging biomarkers from 290 participants with Huntington disease. We used then the resulting HD COURSE MAP to forecast clinical endpoints from the baseline data of 11,510 participants from ENROLL-HD, an external validation cohort. We used such forecasts to select participants at risk for progression and compute the power of trials for such an enriched population.
Then, we used HD COURSE MAP to forecast the progression of endpoints from the baseline measurements of each HD-pathological expansion carriers in an independent data set: ENROLL-HD. We assessed the ability of these forecasts to accurately identify the participants whose selected endpoint experienced a significant progression during a given period. With the same model, we could seamlessly evaluate several endpoints and trial duration at various disease stages. We therefore simulated several hypothetical trials by varying outcome, duration and disease stage. We computed sample sizes using our enriched population and compared them with standard inclusion criteria and enrichment based on CAP and PIN.
Results We used data from 290 participants in TRACK-HD and TRACK-ON studies (1413 visits). TRACK-HD followed 131 pre-symptomatic HD-mutation carriers, 126 patients with early-stage HD and 4 with an unknown status at baseline, to which we added 29 pre-symptomatic HD-mutation carriers from the follow-up study TRACK-ON. These studies also included 186 "control" subjects, most of whom were relatives without a pathogenic HD-mutation. We used data of 11,510 participants from ENROLL-HD with a total number of 28,189 visits as external validation set (see Table  1 ). We selected 15 features in our initial modelling of biomarkers' progression based on TRACK-HD and TRACK-ON participants: • Clinical (1) TMS, the total motor score from the Unified Huntington disease Rating Scale [UHDRS], (2)  Total Functional Capacity [TFC] from the UHDRS and (3) the apathy rating from the Problem Behavior Assessment [PBA-Apathy].
The best-fit parameters predict a personalized trajectory of changes of the subject's biomarkers. The predicted values are compared to the hidden subject's data at follow-up visits 1-4 years after baseline. changes (Fig. 2C ). The first two parameters (time-shift τ and acceleration α) summarize differences in the timing and rate of progression. Together they reflect the Huntingtonian Age (HA). The intermarker spacing parameters (ω j ) represent the trajectory of individual biomarker j, accounting for individual phenotypic differences at a given HA. Together with the general trajectory determined by our analysis, these parameters allowed us to describe the progression of each biomarker for each participant. By comparing these parameters across subjects, we examined the influence of socio-demographics and genetics.
Discussion We applied HD COURSE MAP, a machine learning technique, to capture the detailed progression of clinical, cognitive, and imaging biomarkers during the course of HD. Once trained, our method was able to forecast biomarker values for a new participant from a single baseline visit. HD COURSE MAP, applied to clinical trials, would allow to identify individuals most likely to progress and enrich the tested trial population. We have used data from TRACK-HD and TRACK-ON, two carefully controlled studies across four experienced research sites with low variability between sites  [15] [16] [17] [18] [19]. The low variability between sites also gave us confidence in the repeated measurements in the 7 years spanned by the two follow up studies. Training with only 290 subjects from TRACK-HD and TRACK-ON was sufficient to forecast detailed outcomes for 11,510 participants in ENROLL-HD excluding the imaging since it was not available from the ENROLL-HD data.
HD COURSE MAP also informs about the choice of the outcome according to the targeted disease stage. For instance, it shows that TFC decline is a feature of manifest HD only. We therefore recommend not using TFC as an endpoint for trials with premanifest individuals, since TFC flattens until disease onset. By contrast, TFC was used successfully as outcome for trials targeting manifest HD participants. The concentrations of neurofilament light chain in blood and in CSF have emerged as useful biomarkers for axonal damage prior to clinical changes  29. The same holds true for promising neuroimaging polymarker including structural and functional modalities in the premanifest stages  30. In the dataset used we did not include these biomarkers but they might further enhance the predictive value of HD COURSE MAP and this could be actual limitation.
Methods Data sets. The study used data from two multicentre observational studies: TRACK-HD and TRACK-ON  [15] [16] [17] [18] [19]. These studies measured motor, cognitive, and neuropsychological biomarkers. They also determined the volumes of several brain structures  33, 34. The purpose of these studies was to identify endpoints sensitive to disease progression, starting well before anticipated clinical onset. To validate our prediction method in an independent cohort, we used data from ENROLL-HD, a global clinical research platform designed to facilitate clinical research in HD. We obtained data from the portal https:// www. enroll-hd. org/. Informed consent forms were signed by the participants or a legally acceptable representative (https:// www. enroll-hd. org/ enrol lhd_ docum ents/ Enroll-HD-Proto col-1.