dataesr/scientific-paragraphs-categorization huggingface dataset:
Custom units defined for PSSdb included the pixel per µm and pixel per mm used to convert pixel-based size estimates to metric-based estimates (https://github. com/jessluo/PSSdb/blob/main/scripts/units_def. txt). After standardization, an interactive report is generated to check that units were correctly assigned by displaying the NBSS computed according to section 2. 2. 6, and the average particle size/concentration for individual samples. PSSdb developers can then check that both the size range and the overall concentration recorded are consistent with the particle size targeted by specific instruments  (Lombard et al. , 2019). This step ensures that file format and units in all datafiles are consistent, enabling the further merging of the data in the following PSSdb workflow steps.
Quality control of imaging datasets After morphometric measurements, taxonomic annotations and metadata from the imaging data streams have been downloaded (see 2. 2. 2), the standardizer spreadsheets filled (see 2. The first flagging criterion stands for missing required data or metadata, as specified in the standardizer spreadsheets. Second and third, GPS coordinates are checked to verify whether they are located on land, according to the georeferenced Global Oceans and Seas dataset (v1 automatically downloaded from https://www. marineregions. org/), or located at 0x0 • latitude/longitude, which sometimes indicates that this information has not been filled correctly. Fourth, to determine whether the number of ROIs (n) in a sample was sufficient to accurately estimate a size spectrum, we estimated count uncertainty assuming that particle detection followed a Poisson distribution  (Schartau et al.
, 2010; Bisson et al. , 2022). According to this distribution, the accuracy of ROI counts decreases significantly with lower count numbers n. We could thus estimate the probability of effectively observing n ROIs given that the mean occurrence (the main parameter of the Poisson distribution) was equal to n, and assigned a flag to samples whose ROI counts yielded more than 5% uncertainty. Fifth, the percentage of manual taxonomic annotations (verified by a human expert) is calculated in order to flag samples that are less than 95% validated. This criteria is only applied to scanners and UVP datasets, as the larger number of IFCB images per sample make it more difficult to manually validate automated classifications. Sixth, the percentage of artefacts per sample is evaluated using the predicted or validated annotations so that any sample with 20% or more artefacts is flagged.
Finally, samples with multiple pixel size factors are also flagged, since we do not expect the camera to be re-calibrated or replaced during deployment. After the completion of the QC, a table summarising individual samples and their flags, along with an interactive report providing an overview of the samples flagged for each dataset, are automatically generated. The interactive report is checked by PSSdb developers and sent to the data providers, for an overview of the dataset sample locations, the number of ROIs, the percentages of validation/artefacts per sample, and the overall percentage of flagged samples. Hyperlinks are inserted in the interactive report to verify the sample information directly from the data source. Flags may be overruled by the data provider if they consider a sample is suitable (or not) for ingestion into PSSdb.
Binning of imaging datasets After standardization and QC, we first selected datasets where the sampling depth was between 0 and 250 m. Samples were aggregated spatially in 0. 5x0. 5 • latitude/longitude cells, and temporally per week. This data aggregation approach allowed to (1) increase the overall volume analyzed per sample, which increases the number of particles observed and decreases the instrumental detection limit, and (2) avoid the over-representation of data from fixed time-series stations with high temporal sampling, compared to co-located "snapshot" samples in a given grid cell. The size spectra calculations described in the next section were performed on these weekly, 0. 5x0. we assigned a unique month to each week by selecting the month that counted most samples. This approach prevented the creation of duplicate weekly samples per year.
2. 2. 6 Computation of bulk particle size spectra and regression parameters from binned, instrument-specific datasets The particle size classes used in PSSdb were previously defined in  Kiko et al. (2022). These are logarithmically spaced using a base 2 and an increment of 1/3, so that a doubling in equivalent circular diameter (ECD) is observed every third bin (equivalent to a doubling in biovolume observed every bin), and range between 1-50,000,000 µm. The diameter of each particle, with the exception of artefacts which are excluded from the size spectra computation, was estimated using area according to Eq. (  1 ), and then converted to biovolume assuming a spherical shape of that diameter following Eq. (  2 ). ECD = 2 * area π (1) Biovolume = 1 6 * π * ECD 3 (2) Area-based biovolume, rather than the more widely used distance-map estimates for IFCB datasets  (Moberg and Sosik, 2012; Dubois et al.
, 2022) , and ellipsoidal fits for scanners and UVP datasets, was selected to keep the size spectra calculations consistent across instruments. However, a sensitivity analysis of the slopes and intercepts as a function of the different size proxies (ellipsoidal, distance map, and area-based biovolume) is presented in (Fig. A2 ). Despite some differences in size spectra thresholding, likely due to elongated particles being assigned to different size classes (Fig. A2 a, b  & c ), our sensitivity analysis does not show any substantial differences in size spectra parameters from different biovolume estimates (Fig. A2 d, e  & f). This aligns with previous comparisons of elliptical or spherical biovolume derived size spectra which found no or little statistical difference between these estimates  (Vandromme et al.
, 2012; Dubois et al. , 2022). The database includes size spectra calculated by two widely used methods: the Normalized Biovolume Size Spectrum (NBSS), routinely reported in zooplankton studies (e. g. , San  Martin et al. , 2006) , and the Particle Size Distribution (PSD), calculated from particle counters (broadly) or derived from satellite algorithms  (Kostadinov et al. , 2009; Kiko et al. , 2022). For NBSS, the Normalized Biovolume (NB) (µm 3 L -1 µm -3 ) for each biovolume size class (i) in a sample (0. 5x0. 5 • grid cell, grouped by week) was calculated as the summed biovolume (µm 3 ), normalized by the cumulative volume sampled (L) and the Biovolume bin width (µm 3 ) as in Eq. (  3  (3) For PSD, the Normalized Abundance (NA) (number of particles L -1 µm -1 ) for each size class (i) in a sample was calculated as the total number of particles in ECD size class i, normalized by the cumulative volume sampled (L) and the ECD bin width (µm) as in Eq.
Spatio-temporal coverage of imaging datasets Up to 92,472 individual samples are included in the first release of PSSdb, which benefited from long-term IFCB timeseries collected at a 20 min frequency (Table  1 ). In comparison, the UVP and scanner datasets comprise fewer profiles/nets, with a total of 3,068 profiles and 2,411 net samples, respectively. These datasets span all major ocean basins, although most oceans are undersampled in the southern hemisphere. IFCB datasets were all restricted to the mid-to high-latitudes of the northern hemisphere (Fig. 2 , Table  1 ). Further, the majority of IFCB samples are located on the shelf of the Eastern and Western United States, due to the presence of long-term time-series sites of the California Ocean Observing System and the Northeast U. S. Shelf Long-Term Ecological Research programs (Fig.
2a ). UVP and scanner datasets are distributed more evenly across the ocean basins, mostly due to the Tara Ocean  (2009) (2010) (2011) (2012)  and Tara Polar Circle (2013) global expeditions, even though specific monitoring programs increased the density of samples in the tropical Atlantic, the eastern temperate Atlantic and the Mediterranean Sea (Fig. 2b, c ). These monitoring programs resulted in a large temporal coverage of the three instrument-specific datasets, with repeated observations sustained for periods of 10-25 years (Fig. 2d, e, f ; Table  1 ). Notably, the scanners show the largest temporal coverage, from 1996 to 2022, by including samples collected at the long-term monitoring sites located in the Bay of Villefranche-sur-mer and the Bay of Biscay (France). The gap observed between 1998 and 2003 is caused by the exclusion of samples that had not been validated to at least 95%.
This high-frequency dataset affected the monthly variability of scanners sample density, shown in Fig. 2e , since the Bay of Biscay monitoring program only takes place in May  (Grandrémy et al. , 2023a). UVP datasets have the second longest coverage with observations collected between 2008-2021 (Fig. 2f ). In PSSdb first release, the climatology of UVP sampling density is slightly biased towards spring months (March, April and May), however, this may not reflect actual sampling efforts, as UVP images also need to be more than 95% validated to be ingested in PSSdb. This threshold is not applied to IFCB datasets, which comprise too many images to be manually curated, yet the datasets also show a strong bias towards the summer months (June,  Table  2. Size spectra description for each instrument included in the first release of PSSdb.
Size spectra obtained from individual imaging devices The mean values of the size spectra derived parameters (i. e. , regression slope, intercept, and determination coefficient, or R 2 ) per instrument, as well as the effective size range sampled by each instrument are reported in Table  2. The IFCB effectively detects and images plankton and detrital particles in the nano (2-20 µm) and micro (20-200 µm) size fractions. This size range is supplemented by UVP and scanner datasets, which include predominantly living microplankton (20-200 µm), mesoplankton (200-2,000 µm) and macroplankton (2,000-20,000 µm). The UVP additionally samples fragile organisms and non-living particles, which are disrupted by the net collection (e. g. ,  Biard et al. , 2016; Soviadan et al. , 2023). As a result, the UVP-specific spectra were consistently higher than the scanner spectra.
6 ). This excludes the Arctic Ocean, Red Sea, South Atlantic, Southern Ocean and Baltic Sea, which are represented in PSSdb, but do not have enough data to resolve seasonal cycles. Time series analysis of the instrument-specific NBSS showed pronounced seasonal cycles, but high variability by region, and in some cases, between instruments. Seasonal variations in NBSS parameters were apparent for most ocean basins, as well as in the Mediterranean Sea  (Romagnan, 2013) , which showed high variation of scanner mean slope and intercept through the year (Fig. 6b, g ), with rather constant R 2 values throughout the year (Fig. 6l ). Stable R 2 values were generally observed across all instruments and ocean basins, with the exception of the scanner datasets located in the Indian Ocean, which presented a large dip in NBSS linearity in October (Fig.
6k ). Interestingly, the North Atlantic presented opposite trends between the IFCB and scanner, whose NBSS slopes decreased (i. e. , steepened) and intercepts increased during the spring (Fig. 6c, h ), and the UVP datasets, where NBSS slopes increased (i. e. , flattened) and intercepts decreased during spring and summer (Fig. 6c, h ). In the southern hemisphere, UVP slopes were at a minimum by the end of austral summer (January-May), with a concurrent increase in NBSS intercepts only observed by the end of this period (Fig. 6e, j ). Scanner datasets showed similar trends to that of the UVP in the South Pacific, except that the minimum slope and maximum intercept were observed by March, earlier in the year. The Indian Ocean followed the same seasonal cycle, with large differences between seasons as slopes decreased (i.
e. , steepened) and intercepts increased during the spring-summer transition, and remained relatively stable at high slope and low intercept values from September through November (Fig. 6a, f ). Lastly, the IFCB datasets collected in the North Pacific presented two peaks in NBSS intercepts, with concurrent dips of slopes indicative of steeper NBSS, by spring (April) and fall (Oct) (Fig. 6d, i ). Given the opposite spatial (Fig. 4 , 5) and temporal (Fig. 6 ) patterns often observed between the size spectra slopes and intercepts, across instruments and oceanic regions, we used the yearly time series correlation of these 2 parameters in any given grid cell within the same oceanic region as a way to detect potential decoupling, lag, or feedback between the two. The (de)-coupling between NBSS slopes, which represent the balance between relatively small and large particle and plankton, and intercepts, which approximates the carrying capacity of a given ecosystem, across the years is presented in Figure  7.
480 Noticeably though, there are also areas of strong positive relationship between the two parameters, especially within the IFCB datasets located in the North Atlantic. Flatter NBSS were thus associated with increased abundances of 1 µm 3 organisms, which parameters for 2-3 years, as indicated by grid cells with low absolute correlation coefficients. A de-coupling between size spectra parameters could arise from temporal lag in trophic transfer and complex trophic cascading, similar the one mentioned above. Care should be taken when testing for significant long-term trends in the coupling of the NBSS parameters and detecting yearly perturbations, however we expect such analysis to become more robust as more datasets are ingested into the future releases of PSSdb. Workflows that provide estimates of plankton size distributions with an extensive spatial and temporal coverage will greatly accelerate efforts to characterize and understand ecological plankton dynamics at a global scale.
With this goal, the first PSSdb data products were generated to determine patterns in trophic transfer efficiency across plankton sizes and ecosystem carrying capacity, through consistent measurements of particle sizes analyzed by three state-of-the-art plankton imaging devices. In this section, we discuss how the spatial and temporal coverage of these instrument-specific datasets effectively reduce the gap of available size structure observations, before presenting potential uses of the datasets and future directions for the database. 4. 1 The contribution of PSSdb and other data compilations in reducing the gap of available size structure estimates A global compilation of particle size distribution has been published recently by  Kiko et al. (2022) , using the UVP5 bulk particle size distribution accessible from EcoPart.
Other recent studies (e. g. ,  Hatton et al. , 2021)  have also constructed global estimates of size distribution in marine organisms using indirect biomass estimates of arbitrary plankton size classes derived from satellite proxies, models,or data compilations like COPEPOD  (Moriarty and O'Brien, 2013)  and the MARine Ecosystem biomass DATa (MAREDAT,  Buitenhuis et al. , 2013) , without relying on direct size estimates. Such databases and compilation efforts have benefited from exponentially growing sampling efforts during the past decades, with hundreds to thousands of new UVP profiles generated each year  (Kiko et al. , 2022 ). Yet, to our knowledge, our workflow is the first attempt to compile the counts, size measurements, and taxonomic information of individual particles from multiple imaging devices to generate global particle and planktonic size spectra datasets, aimed to be accessible to the broad scientific community.
Similar to the COPEPOD database, we have focused our effort on compiling data from different instruments, sampling regimes, and data collection methodologies in a self-consistent and cross-calibrated manner, enabling ease of comparison between all the major ocean basins and across sampling systems. So far, size spectrum studies have been restricted to accessible areas and clement weather conditions  (Hatton et al. , 2021) , leading to fewer sampling efforts at high latitudes, specifically the Southern Ocean and the South Pacific Ocean. Similarly, the sampling density is skewed towards the continental shelves, as opposed to open ocean stations. Like other global compilations, our datasets of marine pelagic size structure highlight multiple undersampled regions by plankton imaging systems. All imaging sensors were mostly deployed in the Northern hemisphere, in contrast with fewer deployments in the South Pacific, the Western Indian Ocean, and the Southern Ocean.
While the latter is covered in the UVP-based compilation of particle size structure  (Kiko et al. , 2022) , the absence of the Southern Ocean in our database results from the need for the manual validation of taxonomic annotations to pass our current quality control. However, as more autonomous vehicles will be equipped with UVP6 and their embedded classifier  (Ricour, 2023) , notably in the Bio-Argo program  (Claustre et al. , 2020) , we anticipate that UVP6-derived datasets will grow substantially in the upcoming decade. To accommodate the growth of datasets derived from large-scale surveys, we could relax such criteria to generate specific data products in near-real time. Since a few UVP6 datasets are already incorporated in this initial release, we expect further ingestion of additional UVP6 data to be relatively straightforward.
Unlike UVP datasets, IFCB and scanner datasets are more difficult to compile, due to the lack of a common platform to manage incoming datasets, and increased efforts needed during the sampling (e. g. , net deployments and recoveries), preprocessing (e. g. , concentration or size-fractionation of particles before imaging) and post-processing steps. Notably, the number of images collected at hourly/sub-hourly frequency by IFCB devices and their classification is a veritable bottleneck to produce near real-time datasets. Only 16% of accessible datasets found in our IFCB data sources were ingested in PSSdb due to the lack of taxonomic predictions (needed for detection of artifacts), although we expect more datasets to provide such predictions soon with the rapid development of new classifiers.
Our datasets were thus restricted to the Northern latitudes, highlighting a need for future sampling efforts targeting the Southern hemisphere like BIO-GO-SHIP  (Clayton et al. , 2022). To overcome this limitation, user-specific settings that trigger the image acquisition based on a specific size/fluorescence value may help reduce the total number of images to classify and the presence of smaller cells (4-7 µm) that are harder to identify even manually. Alternatively, newer, more efficient, automated classifiers can also help manage upcoming observations  (Kraft et al. , 2022). 4. 2 Global patterns and trends in plankton size spectrum: insights from PSSdb first release and potential future uses Plankton and particle size spectrum derived parameters (slope, intercept, and determination coefficients) are all important indicators of ecological processes  (Sprules and Munawar, 1986; Trudnowska et al.
, 2010; Banas, 2011; Rossberg et al. , 2019) , and as the result of mesoscale circulation  (Noyon et al. , 2022)  or the omission of specific groups in the observed size range (e. g. , heterotrophic nanoflagellates not detected by most imaging flow cytometers targeting fluorescing organisms, see  Chisholm, 1992). Non steady-state conditions are increasingly observed, particularly in nutrient-rich systems  (Cavender-Bares et al. , 2001) , and represent a considerable interest for environmental policies. For this reason, we carefully assessed and reported size spectra non-linearity in our database, along with the other, widely analyzed, parameters. Our first-release products show that regions with lower R 2 were mostly located toward the North Pole, and were particularly linked to lower (e. g.
However, we hope PSSdb will be further exploited by individual research groups or stakeholders to contextualize their study or policies. In addition, current modelled  (Serra-Pompei et al. , 2022)  and satellite-derived  (Hirata et al. , 2011; Roy et al. , 2013; Kostadinov et al. , 2023)  plankton size distribution have yet to be compared to extensive size structure observations. PSSdb could represent a potential avenue to assess the performance of models and satellite proxies, especially as new and future model outputs  (Negrete-García et al. , 2022)  and satellite datasets (PACE, https://pace. oceansciences. org/) will provide biomass measurements for an ever increasing number of plankton functional groups. Such validation is key to constraining some of their uncertainties, and gain a mechanistic understanding of how physiological and ecological processes structure current and future marine ecosystems.
PSSdb successes, challenges and further considerations to maintain and expand the database In our effort to access and compile imaging datasets from multiple devices, we found the open source platforms (and associated APIs) developed for IFCB, UVP, and scanner users to manage their incoming datasets instrumental. For example, the online dashboards are a useful tool for IFCB data generators to assess image quality during and post-deployment, by quickly checking the raw images and monitoring the number of ROIs per sample, and alert potential stakeholders when a species of interest is detected. However, the possibility to link a set of metadata and a tag (e. g. , in case of suspicion of any bias) for each sample was only added recently on second-generation dashboards. As a result, a significant number of datasets accessible on first-generation IFCB dashboards were not ingested in this initial release.
It is difficult to assess how many IFCB samples were not ingested due to such lack of metadata, as an exhaustive list of IFCB dashboards, that would enable better data traceability, is still missing. Similarly, a portion of scanner and other net-collected imaging datasets are not easily traceable or usable for PSSdb, as some data collectors still use early tools (Zooprocess and PlanktonIdentifier which is no longer supported) to manage their datasets. Even though our pipeline is able to ingest datasets directly sent to us, these datasets are eventually harder to trace and compile compared to UVP datasets which are, to our knowledge, all uploaded on EcoTaxa and EcoPart. Both web platforms offer a secured, easy, and reproducible access to numerous datasets, and for EcoTaxa, to images annotations, a key feature to follow the status of the UVP and scanner datasets that should be validated to at least 95% to be ingested in PSSdb.
These open source management platforms have been available to the scientific community for a decade, but still suffer from a general lack of funding to support their development and maintenance. This contrasts with the increasing funding to develop new imaging prototypes and commercial instruments  (Lombard et al. , 2019; Martin-Cabrera et al. , 2022). Examples of imaging instruments that were not ingested in the PSSdb initial release include the Planktoscope  (Pollina et al. , 2022) , the CytoSense  (Dubelaar and Gerritzen, 2000) , the FlowCam  (Sieracki et al. , 1998) , the ZooGlider  (Ohman et al. , 2019) , the ISIIS  (Cowen and Guigand, 2008) , the CPICS  (Gallager, 2016) , the VPR  (Davis et al. , 2005) , and the LOKI  (Schulz et al. , 2010). From their associated publications, it is unclear how these datasets are archived in long-term repositories, although a few datasets collected with Planktoscope, ZooCAM, CytoSense, and FlowCam instruments have already been uploaded on EcoTaxa.