dataesr/scientific-paragraphs-categorization huggingface dataset:
The radiometric data for each GLORIA entry may be from a single measurement, or the mean or median of several measurements over a time interval. When available, the data contributors provided the spectral R rs means, standard deviations, and numbers of measurements for sampling events. Quality control was conducted on all received spectra (see section Technical validation). The measurement setups and instruments used for radiometric measurements are listed below. The number of the method corresponds to the column 'Measurement_method' in GLORIA_meta_and_lab. csv. References to published descriptions of the approach and applications are provided where available.
L u (0-) and E s on pole connected to a spectrometer via fiber optics from MP or water edge Instruments: Ocean Insight/Ocean Optics USB2000/USB2000 + /USB4000 SeaBaSS data. GLORIA includes approximately 1100 entries from SeaBASS  33. We searched SeaBASS for reflectance spectra with concomitant water quality measurements and ensured that these are from inland and coastal waters only by mapping sampling locations of all records from water depths less than 200 m. Where water depth was not part of the SeaBASS record, we assigned it based on the General Bathymetric Chart of the Ocean (GEBCO_2021 Grid sub-ice topo/bathy)  77. Several metadata fields were unavailable for this data, but SeaBASS dataset identifiers are provided to allow further research if needed. All SeaBASS data were included in our quality control process.
While SeaBASS allows the upload of uncertainty data for radiometry and water quality, the entries we located for inland and coastal waters did not contain this information. Water sample analysis. Water quality attributes Chla, TSS and a CDOM (440) were determined using well established high-accuracy laboratory methods. The method for each analysis is identified in the columns 'Chl_ method' , 'TSS_method' , and 'aCDOM_method' in the file GLORIA_meta_and_lab. csv and method details are provided in GLORIA_variables_and_methods. xlsx. Where available, data means and standard deviations from replicate analyses of Chla, TSS and a CDOM (440) are provided in separate files. The most frequently used methods for Chla were via solvent-based pigment extraction from filter pads followed by fluorometric (U.
2 μm pore size polycarbonate membranes to remove particulates, was measured in a spectrophotometer and converted to absorption. Secchi depth was determined as the depth at which a disk, typically black and white of 20 or 30 cm in diameter, is no longer visible by an observer when it is lowered into the water  82, 83. ancillary and metadata. Each data entry is associated with fields identifying the data contributor, cross-references to other databases, and details describing the sampling site and environmental conditions. Several categorical variables allow cursory stratification of the dataset according to water body type (lake, estuary, coastal ocean, river or other), data collection purpose (e. g. , routine surface water monitoring or event-driven sampling), dominant biogeochemical water type (e.
Data Records The GLORIA dataset is hosted at the PANGAEA Data Publisher for Earth & Environmental Science  84. The data is contained in several comma-separated value (csv) files and a Microsoft Excel file provides keys to column names and method details (Table  1 ). Individual data points are identified across all files using the GLORIA_ID. The 7,572 GLORIA R rs spectra originate from 31 countries over an almost global geographical range from 67°N to 54°S and from 122°W to 178°E (Fig. 2 ) with the majority of samples from lakes (60%), followed by coastal waters (32%), estuaries (4%), and the remainder from rivers and other water body types. The wide range of radiometric and water quality measurements in GLORIA (Fig. 2 ) is consistent with the global diversity of R rs spectral shapes with respect to optical water types  85, 86  and visual color ranges  87, 88  (Fig.
technical Validation All data submitted for inclusion into this compilation had undergone quality control by the providers. Our curation process included detailed information recovery with them to ensure sampling, sample processing, and laboratory analysis methods are fit for purpose. Further checks on the gathered data were carried out as described below. Reflectance spectra. Reflectance spectra were checked for outliers and unrealistic spectral shapes using a series of quality control indicators (Table  2 ). By flagging, but keeping, spectra with moderate or suspected quality issues, we were able to retain a larger dataset and we advise the user to inspect the flags to evaluate the dataset for their purposes. The quality control methods are described below. Data entries with quality issues are identified by setting the corresponding quality flag to one (1) in the file GLORIA_qc_flags.
csv. The first round of quality control was a procedural detection of high-frequency variability (suspected noise), baseline shifts (e. g. , from suboptimal glint removal), the presence of an oxygen absorption feature near 762 nm (e. g. , from sensor intercalibration issues), and negative slopes in the ultraviolet to blue part of the spectrum (e. g. , from suboptimal diffuse sky radiance correction). These are the first five flags in Table  2. Additionally, we calculated the Quality Water Index Polynomial (QWIP) score  89. This approach was developed to identify hyperspectral aquatic reflectance data that fall outside general trends observed in a large dataset from optically deep waters. Briefly, the QWIP is a 4th order polynomial which describes a well-formed central tendency for a spectrally integrated metric (Apparent Visible Wavelength  90  , AVW) to predict a Normalized Difference Index (NDI; λ = 492, 665 nm) across a continuum of water types.
GLORIA_variables_and_methods. xlsx Excel file with several sheets: Data headers: Key to columns and units in the ancillary and metadata table (GLORIA_meta_and_ lab. csv). Radiometry methods: Details of the instruments and their setups for the radiometric measurements. Chla methods, TSS methods, aCDOM Methods: Method details for the respective water quality measurements. Cross-referencing to data entries requires the dataset ID and methodology name from the table GLORIA_meta_and_lab. csv. References: List of references cited in this file.
GLORIA_waterqual_uncert Mean, standard deviation and number of replicates for water quality measurements. The replicate type is specified as 'field' (separate water samples taken in the field) or 'lab' (replicate analyses of the same water sample). This information is available for a subset of the dataset. Table  1. showing the visual color derived from each GLORIA R rs spectrum using the tristimulus weighting functions according to the Commission Internationale de l'Éclairage (CIE)  99  ; WP: white point. consistency across the batches from different individuals. The resulting set of suspect spectra are identified by the flag 'Suspect' in the file GLORIA_qc_flags. csv (Table  2 ). Uncertainty in R rs from above-surface measurements by means of reconstruction with a coupled water-atmospheric radiance model.
To this end, we used the 3C algorithm  92  to reconstruct R rs from records where L t , L sky and E s were available. 3C provides a reconstruction of R rs using nonlinear optimization of atmospheric and water optical models, allowing for a range of optical properties to solve the relationship between the upwelling radiance and downwelling irradiance provided as input. Due to the flexibility of the surface corrections, 3C is proposed to enable robust R rs to be obtained across a wide range of measurement conditions. The resultant 3C-R rs is expected to have reduced propagation of error from the variable spectral shape of sky reflectance and glint. This provides an advantage over methods which consider these corrections either constant, or a function of wind speed  60  , which is the case for the majority of R rs from above-surface measurements reported in the GLORIA database (column 'Skyglint_removal' in GLORIA_meta_and_lab.
Noisy_blue 15 High-frequency variability, potentially instrument noise, near the blue end: spectra were standardized to zero mean and unit standard deviation. A 4th order polynomial was fitted over the interval 350-400 nm. Cases where root-mean square error >0. 15 were flagged (threshold determined using visual inspection of the distribution of RMSEs with respect to spectral shapes). Baseline_shift 164 Spectra shifted up are those where the minimum R rs is 60% of its median. This percentage corresponds approximately to 1. 5 times the interquartile range above the upper quartile of the baseline-percent distribution of the entire GLORIA dataset. Spectra shifted down are those with at least 20 negative values and either: • a negative linear slope in the interval 765-900 nm <-8. 75 × 10 -7 sr -1 nm -1 (the slope threshold was determined as the bound of the lower quartile) and >50% negative R rs values in this spectral region; or • >70% negative R rs in the interval 765 nm-900 nm; or • at least 20 negative R rs at in the interval 350-450 nm.
QWIP_score The QWIP score represents the difference between a calculated Normalized Difference Index (NDI; λ = 492, 665 nm) and that estimated empirically from the spectrum's Apparent Visible Wavelength (AVW). Data producing absolute QWIP scores exceeding a value of ±0. 2 have been found to exhibit spectral shapes that deviate from central tendencies typically observed in aquatic reflectance data. For this analysis, we used the 1589 spectra which included L t , L sky , E s , observation time, and geographic location, and for which the method to calculate R rs was not already based on 3C. This analysis is also independent from the quality flagging in the previous section, so that all observations were included and the results present a worst-case scenario which best represents the algorithmic uncertainty inherent to calculating R rs , albeit without knowledge of quality control criteria applied prior to the data being reported.
In relative terms (Fig. 4B ), median bias in R rs between observed and 3C-R rs is smallest in the green spectral range (order of 6. 4%), where peak R rs amplitude is typically observed in this dataset, and largest in the UV and NIR regions of the spectrum where R rs is typically lower. The spread (interquartile range) in the relative bias in R rs (560) is 5-16%, but much wider in the UV and NIR range, exceeding -30% and 170%. The largest differences in R rs bias between reported and 3C spectra were found between contributed datasets, rather than between observation methods. The majority of datasets showed absolute relative differences in R rs (400-800) in the 0-10% range, but there are also cases where the difference exceeds 100%. This analysis points to an overall high degree of uncertainty in the methods using above-water L t measurements and the need for rigorous quality control by observers.
For future work, we suggest adding R rs model reconstruction as part of the data collection effort, which allows inspection of glint terms to objectively flag observations as suspect, before other quality controls are implemented. Furthermore, to support future algorithmic improvements (e. g. , to elaborate bidirectional reflectance distribution functions), all component spectra and observation geometries should be included in datasets and these should be reported at the native resolution of each sensor involved to avoid convolution error when calculating R rs 94. Water quality. The water quality measurements were investigated using frequency distributions to identify outliers. Separate frequency distributions were created by 'Water_type' , a subjective classification assigned by the data contributors according to the dominant optical constituent for each water body (TSS-dominated, Chla-dominated, CDOM-dominated, Chla + CDOM-dominated, moderately turbid coastal, clear).
Usage Notes References to method details. The methods used for radiometric measurements and laboratory analyses are identified in the columns 'Measurement_method' , 'Chl_method' , 'TSS_method' , and 'aCDOM_method' in the file GLORIA_meta_and_lab. csv. Associated details with references are provided in separate sheets in the file GLORIA_variables_and_methods. xlsx. Looking up the method for a particular measurement requires the 'Dataset_ID' and the method name. Quality flags. Each R rs measurement is associated with quality flags (file GLORIA_qc_flags. csv). The quality flags are binary and indicate the presence ('1') or absence ('0') of the quality issue described in Table  2. Missing values indicate that the flag could not be determined because the spectrum did not include the required wavelength range.
Some numerical values generated during the quality control are provided in the file GLORIA_qc_ancillary. csv (Table  3 ). cross-references to other datasets. Some of the data in GLORIA is part of other data publications, or is also included in the community repositories SeaBASS  33  and/or LIMNADES. The columns 'SeaBASS_ID' , 'LIMNADES_ID' , and 'LIMNADES_UID' in the data table (GLORIA_meta_and_lab. csv) provide identifiers used in the respective datasets to facilitate cross referencing entries, for example for the avoidance of duplicates. Other references to prior publication of the data are provided in the 'Comments' column in GLORIA_meta_and_lab. csv in the form of a digital object identifier (DOI).
Acknowledgements Making measurements and taking samples in inland and coastal waters has considerable challenges. These include overland travel to and from water bodies, launching boats across natural shorelines, and processing samples at the end of long days. Therefore, this data is the result of the hard work by many more people than can practically be acknowledged individually here, and we sincerely thank field personnel, lab technicians, students, captains and skippers, and all other supporting personnel without whom this dataset would not have been possible. In some cases, specific acknowledgements of people and/or funding can be found in the 'Comments' column in the file GLORIA_meta_and_lab. csv. Funding sources include:  Estonian Ministry of Education and Research ;  European Commission   FP7 ,  H2020 ,  FP7-ENV-2007-1-226224 ;  Estonian Research Council ;  Helmholtz Infrastructure Initiative FRAM ;  BMBF 03G0218A ;  New Zealand Ministry for Business, Innovation & Employment  grants  UOWX1503 ,  UOWX1802 ,  KENTR1601 ,  NASA  ROSES grants  80HQTR19C0015 ,  80NSSC 21K0499 ,  80NSSC22K1389 , and  USGS Landsat Science Team  Award  140G0118C0011 ,  Vietnam National Foundation for Science and Technology Development (NAFOSTED) , grant number  105.
, 2015). Thus, the differences in the combinatorial pattern of H3K4me moieties likely re ect different transcription levels between the two types of elements, leading to a uni ed model of distal and proximal regulatory elements (Andersson and Sandelin, 2020). Shah et al. , observed that, when averaged across the entire set of the putative regulatory elements, the level of speci city-corrected H3K4me3 is substantially lower at enhancers than at promoters (Figs. 4C and 4H from Shah et al. ), which is in line with published reports in that only a fraction of enhancers is associated with H3K4me3. However, the authors concluded that the apparent enrichment of H3K4me3 at enhancers arises as a consequence of "signal leakage" from lower methyl forms due to the poor quality of the antibodies. Yet, even after speci city-correction of H3K4me3 datasets, a signi cant level of H3K4me3 at enhancers was retained (Fig.
Experimental Procedures Gene Ontology analysis of genes associated with broad domains of H3K4me3 H3K4me3 peaks associated with gene promoters in a subset of samples from ENCODE and Roadmap Epigenomics were retrieved from Chen et al. (Chen et al. , 2015). Genes associated with H3K4me3 peaks broader than 4 kb were extracted for each sample and used for GO Biological Process enrichment analysis using clusterPro ler  (Yu et al. , 2012). For each sample, we retrieved the most signi cantly enriched term related to the biological processes.
Estimation of the transcriptional activity of enhancers Global run-on coupled with high throughput sequencing (GRO-cap) data from K562 cells was used to estimate transcriptional activity at enhancer elements. Process GRO-cap tracks were downloaded from GEO and the signal at enhancers integrated (GRO-cap score). Classi cation of enhancers into expression classes was based on the distribution of the GRO-cap scores and we identi ed elements with no GRO-cap reads, elements with low expression (GRO-cap score < 5 but > than 0) and elements with high level of GRO-cap signal (> 5).
ACKNOWLEDGEMENTS Work in the laboratory of S. Spicuglia was supported by recurrent funding from  Institut National de la Santé et de la Recherche Médicale  and  Aix-Marseille University  and by speci c grants from  INCA  ( PLBIO018-031 INCA_12619 ) and  Ligue Contre le Cancer  (Equipe Labellisée 2018 and 2023). Work in the  Pękowska lab  is funded by the  Dioscuri Grant  (Dioscui is a programme initiated by the  Max Planck Society (MPG) , jointly managed with the  National Science Centre in Poland  (NCN), and mutually funded by the  Polish Ministry of Education and Science  and the  German Federal Ministry of Education and Research (BMBF)) , by the  OPUS17  ( UMO-2019/33/B/NZ2/02437 ) and  Sonata Bis 11  ( UMO-2021/42/E/NZ2/00392 ) grants from the  NCN , and by the  EMBO Installation   Grant.
Declarations Code and data availability: All custom scripts used in this study are available at GitHub (https://github. com/guillaumecharbonnier/mw-pekowska2019). All data used in this study are described in Table  1. Data Dataset File name Broad H3K4me3 peaks (Chen et al. , 2015) (https://media. nature. com/original/natureassets/ng/journal/v47/n10/extref/ng. 3385-S4. xls).
bed H3K4me2 GSM2773400 GSM2773400_AR16-5_TF- Thermo # 710796_mQ20_L200_genome_coverage. bigwig 710796 (abMe2-3) H3K4me2 GSE103543 GSE103543_AR16_H3K4me2-Corrected_HMD. bedgraph corrected HMD H3K4me2 GSM2773397 GSM2773397_AR16-3_AB- HMD 7766_mQ20_L200_genome_coverage. bigwig Abcam # 7766 (abMe2-1) H3K4me2 ENCODE https://www. encodeproject. org/experiments/ENCSR000AKT/ Abcam # 7766 (abMe2-1) H3K4me2 B ENCODE ENCFF945VIU. bigWig cells.
, 2017; Deng et al. , 2019; Schuhmann et al. , 2019; Sokoliuk et al. , 2019; Kasten et al. , 2020). In this study, we analyzed three datasets, two publicly available  (Foster et al. 2017 and Feldmann-Wustefeld et al. 2019 , see below), and one collected specifically for this study. In all dataset, participants attended either to the left or the right hemifield, while keeping central fixation. Our results confirmed the hemispheric modulation of alpha-band oscillations in posterior regions  (Worden et al. , 2000; Sauseng et al. , 2005; Kelly et al. , 2006; Thut et al. , 2006; Händel et al. , 2011)  and revealed two distinct alpha-band traveling waves propagating in opposite directions. First, visual attention increases top-down alpha-band waves propagating from frontal to occipital regions ipsilateral to the attended location, and such waves correlate positively with alpha power in frontal and occipital regions.
E) Simulated data providing a schematic representation of forward and backward waves in the time domain in a given line of electrodes (from more frontal E1 to more occipital E7). A positive and a negative phase shift characterized forward and backward waves, respectively. Attending to visual stimuli modulates traveling waves. In this analysis, we investigated how covert visual attention influences the traveling wave pattern. We focused on trials where neither a target nor a distractor was presented. First, we quantified the amount of traveling waves in the contra-and ipsi-lateral hemispheres to the attentional allocation. As shown in figure 2 (left column), we found a strong lateralization effect revealing an increase (respectively, decrease) of contralateral (ipsilateral) forward waves in the alpha band, and the opposite pattern in waves propagating in the opposite direction.
Covert attention modulates forward waves only with visual stimuli. To confirm our previous results, we replicated the same traveling waves analysis on two publicly available EEG datasets in which participants performed similar attentional tasks (experiment 1 of Foster et al. , 2017 and experiment 1 of Feldmann-Wüstefeld and Vogel, 2019). In the first experiment from the Feldmann-Wüstefeld and Vogel dataset, participants were instructed to perform a visual working memory task in which, while keeping a central fixation, they had to memorize a set of items while ignoring a group of distracting stimuli. We focused our analysis on those trials in which the visual items to remember were placed either to the right or the left side of the screen, while the distractors were either in the upper or lower part of the screen (we pulled together the trials with either 2 or 4 distractors, as this factor was irrelevant for our analysis).
The stimuli were shown for 200ms, and we computed the amount of forward and backward waves in the 500ms following stimulus onset. As shown in figure  2  (central column), the analysis confirmed our previous results, demonstrating a strong interaction between the factors DIRECTION and LATERALITY (BF 10 =667, error~2%; independently, the factors DIRECTION and LATERALITY had BF 10 =0. 2 and BF 10 =0. 4, respectively). These results confirmed that spatial attention modulates both forward and backward waves in the presence of visual stimulation. Next, we analyzed another publicly available dataset from Foster et al. , 2017. In the first experiment of Foster's study, participants completed a spatial cueing task, requiring them to identify a digit among distractor letters. After a central cue was displayed for 250ms, participants attended one of eight locations for 1000ms before the onset of the target and distractors.
e. , without modeling the slope of the random terms), the interaction between DIRECTION and LATERALITY proved significant (F(1,16)=9. 81, p=0. 003,  =0. 13). In addition, when testing LATERALITY separately for forward and backward waves, we observed an effect in the backward waves (BF 10 =3. 497, error<0. 01%) but not in the forward waves (BF 10 =0. 231, error<0. 01%, supporting evidence in favor of the absence of an effect). In addition, as analyzed in our dataset, we tested the correlation between backward waves and alpha-band power in occipital (electrodes: PO3, PO4) and frontal (electrodes: F3, F4) regions. We found moderate evidence of a positive correlation between contra-and ipsi-lateral backward waves, and occipital (all Pearson's r~=0. 4, all BFs 10 ~=3) but inconclusive evidence in the frontal areas (all Pearson's r~=0.
3, all BFs 10 ~=2). These results supported those from our dataset, despite having a smaller amount of electrodes' lines, and potentially reduced statistical power (see figure  2 , lower panels). All in all, we could confirm our previous conclusion that covert visual attention modulates top-down oscillatory waves, showing this effect even in the absence of visual stimulation. In addition, we surmised that the lateralization effect we reported in the forward waves in our dataset (absent in the Foster dataset) is related to the steady visual stimulation during the attentional allocation, in line with our previous results demonstrating that oscillatory bottom-up waves reflect sensory processing  (Alamia and VanRullen, 2019; Pang et al. , 2020). Both detected targets and distractors elicit FW waves, but not missed targets.