dataesr/scientific-paragraphs-categorization huggingface dataset:
02 for the North Pacific pH data in the merged product files. Elsewhere, the inconsistencies that may have arisen are smaller since deep pH is typically higher than 7. 7  (Lauvset et al. , 2020) , and at such levels the difference between calculated and measured pH is less than 0. 01 on average  (Álvarez et al. , 2020; Carter et al. , 2018). Outside the North Pacific, we believe, therefore, that the pH data are consistent to 0. 01. Avoiding interconsistency considerations for these intermediate products helps to reduce the problem, but since the reference dataset (as also used for the generation of the CANYON-B and CONTENT algorithms) has these issues, a full re-evaluation, envisioned for the future GLODAPv3, is needed to address the problem completely.
CANYON-B and CONTENT analyses CANYON-B and CONTENT  (Bittig et al. , 2018)  were used to support decisions regarding application of adjustments (or not). CANYON-B is a neural network for estimating nutri-ents and seawater CO 2 chemistry variables from temperature, salinity, and oxygen concentration. CONTENT additionally considers the consistency among the estimated CO 2 chemistry variables to further refine them. These approaches were developed using the data included in the GLODAPv2 data product (i. e. , the 2016 version without any more recent updates). Their advantage compared to crossover analyses for evaluating consistency among cruise data is that effects of water mass changes on ocean properties are represented in the nonlinear relationships in the underlying neural network. For example, if elevated nutrient values measured on a cruise are not due to a measurement bias but actual aging of the water masses that have been sampled and as such accompanied by a decrease in oxygen concentrations, the measured values and the CANYON-B estimates are likely to be similar.
Vice versa, if the nutrient values are biased, the measured values and CANYON-B predictions will be dissimilar. Used in the correct way and with caution this tool is a powerful supplement to the traditional crossover analyses which form the basis of our analyses. Specifically, we gave no weight to comparisons in which the crossover analyses had suggested that the S and/or O 2 data were biased as this would lead to error in the predicted values. We also considered the uncertainties of the CANYON-B and CONTENT estimates. These uncertainties are determined for each predicted value, and for each comparison the ratio of the difference (between measured and predicted values) to the local uncertainty was used to gauge the comparability. As an example, the CANYON-B and CONTENT analyses of the data obtained for 320620170820 are presented in Fig.
For the other variables, the inconsistencies are low and agree with the crossover results (not shown here, but results can be accessed through the adjustment table ). Another advantage of the CANYON-B and CONTENT comparisons is that these procedures provide estimates at the level of individual data points; e. g. , pH values are determined for every sampling location and depth where T, S, and O 2 data are available. Cases of strong differences between measured and estimated values are always examined. This has helped us to identify primary QC issues for some cruises and variables, for example a case of an inverted pH profile on cruise 32PO20130829, which was identified and amended in GLODAPv2. 2020.
Updates and corrections for GLODAPv2. 2020 Several minor omissions and errors have been identified in the GLODAPv2. 2020 data product since the release in 2020. Most of these have been corrected in this release, but some issues, such as those relating to pH in the North Pacific (Sect. 3. 2. 4), will not be remedied before GLODAPv3. In addition, some recently available data have been added for a few cruises. The changes are as follows: -Individual suspicious samples, identified and reported by users and data providers, have been deleted from the product. This affects oxygen on cruises 31DS19940126 and 29HE20130320; nutrients on cruises 316N19950829 and 06BE20001128; salinity on cruises 06BE20001128, 316N19921006, 318M19730822, 35A319950221, 49K619940107, and 32PO20130829; and TAlk on cruises 58P320011031, 33RO20071215, and 316N19821201.
Merging The new data were merged into a bias-minimized product file following the procedures used for GLODAPv2. 2020 (Olsen et al. , 2020)  with some modifications: -Data from the 43 new cruises were merged and sorted according to EXPOCODE, station, and pressure. GLO-DAP cruise numbers were assigned consecutively, starting from 3001, so they can be distinguished from the GLODAPv2. 2020 cruises, which ended at 2106. -For some cruises the combined concentration of nitrate and nitrite was reported instead of nitrate. If explicit nitrite concentrations were also given, these were subtracted to get the nitrate values. If not, the combined concentration was renamed to nitrate. As nitrite concentrations are very low in the open ocean, this has no practical implications. -When bottom depths were not given, they were approximated as the deepest sample pressure +10 dbar or extracted from ETOPO1  (Amante and Eakins, 2009) , whichever was greater.
For GLODAPv2, bottom depths were extracted from the Terrain Base (National Geophysical Data Center/NESDIS/NOAA/U. S. Department of  Commerce, 1995). The intended use of this variable is only drawing approximate bottom topography for sections. -Whenever temperature was missing in the original data file, all data for that record were removed, and their flags set to 9. The same was done when both pressure and depth were missing. For all surface samples collected using buckets or similar, the bottle number was set to zero. There are some exceptions to this, in particular for cruises that also used Gerard barrels for sampling. These may have valuable tracer data that are not accompanied by a temperature, so such data have been retained. -All data with WOCE quality flags of 3, 4, 5, or 8 were excluded from the product files, and their flags were set to 9.
-Missing seawater CO 2 chemistry variables were calculated whenever possible. The procedures for these calculations have been slightly altered as the product now contains four such variables; earlier versions of GLO-DAPv2  (Olsen et al. , 2016 (Olsen et al. , , 2019) )  included only three, so whenever two were included, the one to calculate was unequivocal. Four CO 2 chemistry variables gives more degrees of freedom in this respect; e. g. , a particular record may have measured data for TCO 2 , TAlk, and pH, and then a choice needs to be made with regard to which pair to use for the calculation of fCO 2. We followed two simple principles. First, TCO 2 and TAlk were the preferred pair to calculate pH and fCO 2 because we have higher confidence in the TCO 2 and TAlk data than pH (given the issues summarized in Sect.
3. 2. 4) and fCO 2 (because it was not subjected to secondary QC). Second, if either TCO 2 or TAlk was missing and both pH and fCO 2 data existed, pH was preferred (because fCO 2 has not been subjected to secondary QC). All other combinations involve only two measured variables. The calculations were conducted using CO2SYS  (Lewis and Wallace, 1998)  for  MAT-LAB (van Heuven et al. , 2011) , with the carbonate dissociation constants of  Lueker et al. (2000) , the bisulfate dissociation constant of  Dickson (1990) , and the borate-to-salinity ratio of  Uppström (1974) , as in GLO-DAPv2. 2020 and earlier versions  (Olsen et al. , 2020). We are aware that the borate-to-salinity ratio of Lee et al. (  2010 ) is becoming the community standard but here maintain  Uppström (1974)  in order to maintain consistency between versions.
For calculations involving TCO 2 , TAlk, and pH, if less than a third of the total number of values, measured and calculated combined, for a specific cruise were measured, then all these were replaced by calculated values. The reason for this is that secondary QC of the few measured values was often not possible in such cases, for example, due to a limited amount of deep data available. Such replacements were not done for calculations involving fCO 2 , as this would either overwrite all measured fCO 2 values or would entail replacing a measured variable that has been sub-jected to secondary QC (i. e. , TCO 2 , TAlk, or pH) with one calculated from a variable that has not been subjected to secondary QC (i. e. , fCO 2 ). Calculated seawater CO 2 chemistry values have been assigned a WOCE flag of 0.
Sensor and bottle data merge for salinity and oxygen Table  4  summarizes the actions taken for the merging of the CTD and bottle data for salinity and oxygen. For 75 % of the 43 new cruises both CTD and bottle data of salinity were included in the original cruise data files, and for all these cruises the two data types were found to be consistent. This is similar to the GLODAPv2. 2020 results. For oxygen, 63 % of the new cruises included both CTD O 2 and bottle values, which is much more than for GLODAPv2. 2020 (25 %) but comparable to GLODAPv2. 2019. Having both CTD and bottle values in the data files is highly preferred as the information is valuable for quality control (bottle mistrips, leaking Niskin bottles, and oxygen sensor drift are among the issues that can be revealed). The extent to which the bottle data (i.
As a consequence, most, but not all, of these data (some being excluded because of poor precision after consultation with the principal investigator) are included with a secondary QC flag of 0. The secondary QC actions for the 12 core variables and the distribution of applied adjustments are summarized in Table  6  and Fig. 6 , respectively. For most variables only a small fraction of the data are adjusted: no salinity or pH data, 4. 5 % of TCO 2 and TAlk data, 7 % of oxygen data, 14 % of nitrate and phosphate data, and 21 % of silicate data. For the CFCs, no data required adjustment. Overall, the magnitudes of the various adjustments applied are also small. There is a larger fraction of data requiring adjustments to nutrients in GLODAPv2. 2021 compared to GLODAPv2. 2020. However, the tendency observed during the production of GLO-DAPv2.
2019 and GLODAPv2. 2020 remains, namely that the large majority of recent cruises are consistent with earlier releases of the GLODAP data product. Only 13 out of the 43 new cruises included measured pH data, and none received an adjustment. However, we have not performed a new crossover and inversion analysis of all pH data in the northwestern Pacific (though such an analysis is planned for the next full update of GLODAP, i. e. , GLODAPv3). Therefore, for now the conclusion from GLO-DAPv2. 2020 remains, and some caution should be exercised if looking at trends in ocean pH in the northwestern Pacific using GLODAPv2. 2020 or GLODAPv2. 2021. For the nutrients, adjustments were applied to maintain consistency with data included in GLODAPv2,  GLO-DAPv2. 2019, and GLODAPv2. 2020. An alternative goal for the adjustments would be maintaining consistency with data from cruises that employed CRMNS to ensure accuracy of.
Adjustment value The data have not been quality controlled, are of uncertain quality, and are suspended until full secondary QC has been carried out. -666 The data are of poor quality and excluded from the data product. -777 The data appear of good quality, but their nature, being from shallow depths and coastal regions without crossovers or similar, prohibits full secondary QC. -888 No data exist for this variable for the cruise in question. -999 * The value of 0 is used for variables with additive adjustments (salinity, TCO 2 , TAlk, pH) and 1 for variables with multiplicative adjustments (for oxygen, nutrients, CFCs). This is mathematically equivalent to "no adjustment" in both cases. nutrient analyses. Such a strategy was adopted by  Aoyama (2020)  for preparation of the Global Nutrients Dataset 2013 (GND13) and is being considered for GLODAP as well.
However, as this would require a re-evaluation of the entire dataset, this will not occur until the next full update of GLO-DAP, i. e. , GLODAPv3. For now, we note the overall agreement between the adjustments applied in these two efforts  (Aoyama, 2020)  and that most disagreements appear to be related to cases to which no adjustments were applied in GLO-DAP. This can be related to the strategy followed for nutrients for GLODAPv2, in which data from GO-SHIP lines were considered more accurate than other data  (Olsen et al. , 2016). CRMNS are used for nutrients on most GO-SHIP lines. The improvement in data consistency due to the secondary QC process is evaluated by comparing the weighted mean of the absolute offsets for all crossovers before and after the adjustments have been applied. This "consistency improvement" for core variables is presented in Table  7.
The data for CFCs were omitted from these analyses for previously discussed reasons (Sect. 3. 2. 6). Globally, the improvement is modest. Considering the initial data quality, this result was expected. However, this does not imply that the data initially were consistent everywhere. Rather, for some regions and variables there are substantial improvements when the adjustments are applied. For example, silicate in the Atlantic Ocean shows a considerable improvement, and nutrients in general show improvements in almost all regions, including globally. The various iterations of GLODAP provide insight into initial data quality covering more than 4 decades. Figure  7  summarizes the applied absolute adjustment magnitude per decade. These distributions are broadly unchanged compared to GLODAPv2. 2020 (Fig.
Data availability The GLODAPv2. 2021 merged and adjusted data product is archived at NOAA NCEI at https://doi. org/10. 25921/ttgq-n825 (Lauvset et al. , 2021). These data and ancillary information are also available via our web pages https: //www. glodap. info and https://www. ncei. noaa. gov/access/ ocean-carbon-data-system/oceans/GLODAPv2_2021/ (last access: 7 July 2021). The data are available as commaseparated ascii files (*. csv) and as binary MATLAB files (*. mat) that use the open-source Hierarchical Data Format version 5 (HDF5). The data product is also made available as an Ocean Data View (ODV) file which can be easily explored using the "webODV Explore" online data service (https://explore. webodv. awi. de/, last access: 7 July 2021). Regional subsets are available for the Arctic, Atlantic, Pacific, and Indian oceans.
There are no data overlaps between regional subsets, and each cruise exists in only one basin file even if data from that cruise crosses basin boundaries. The station locations in each basin file are shown in Fig. 8. The product file variables are listed in Table  1. A lookup table for matching the EXPOCODE of a cruise with GLODAP cruise number is provided with the data files, and a similar table is provided for matching the GLODAP cruise number with the data DOI. In the MATLAB files this information (EX-POCODE and DOI) is available as a cell array. A "known issues document" accompanies the data files and provides an overview of known errors and omissions in the data product files. It is regularly updated, and users are encouraged to inform us whenever any new issues are identified. It is critical that users consult this document whenever the data products are used.
The original cruise files, with updated flags determined during additional primary GLODAP QC, are available through the GLODAPv2. 2021 cruise summary table (CST) hosted by NOAA NCEI: https: //www. ncei. noaa. gov/access/ocean-carbon-data-system/ oceans/GLODAPv2_2021/cruise_table_v2021. html (last access: 7 July 2021). Each of these files has been assigned a DOI, which is included in the data product files but not listed here. The CST also provides brief information on each cruise and access to metadata, cruise reports, and its adjustment table entry. While GLODAPv2. 2021 is made available without any restrictions, users of the data should adhere to the fair data use principles: for investigations that rely on a particular (set of) cruise(s), recognize the contribution of GLODAP data contributors by at least citing the articles where the data are described and, preferably, contacting principal investigators for exploring opportunities for collaboration and co-authorship.
Summary GLODAPv2. 2021 is an update of GLODAPv2. 2020. Data from 43 new cruises have been added to supplement the earlier release and extend temporal coverage by 1 year. GLO-DAP now includes 47 years, 1972-2020, of global interior ocean biogeochemical data from 989 cruises. The total number of data records is 1 334 269. Records with measurements for all 12 core variables (salinity, oxygen, nitrate, silicate, phosphate, TCO 2 , TAlk, pH, CFC-11, CFC-12, CFC-113, and CCl 4 ) are very rare; only 2029 records have measured data for all 12 in the merged product file (interpolated and calculated data excluded). Requiring only two out of the four measured seawater CO 2 chemistry variables, in addition to all the other core variables, brings the number of available records up to 9231, and so this is also very rare.
A major limiting factor to having all core variables is the si- multaneous availability of data for all four transient tracer species: only 26 137 records have measurements of CFC-11, CFC-12, CFC-113, and CCl 4 while 422 029 have data for at least one of these (not considering availability of other core variables). A total of 423 544 records have measured data for two out of the three CO 2 chemistry core variables. The number of records of measured fCO 2 data is 33 844; note again that these data were not subjected to quality control. The number of records with measured data for salinity, oxygen, and nutrients is 832 566, while the number of records with salinity and oxygen data is 1 127 477. All of the above numbers concern measured data, not interpolated or calculated values. A total of 2 % (27 538) of the total data records do not have salinity.
There are several reasons for this, the main one being the inability to vertically interpolate due to a separation that is too large (Sect. 3. 3. 2) between measured samples. Other reasons for missing salinity include salinity not being reported and missing depth or pressure. Note that there are slightly fewer records with fCO 2 and all CFC data in GLO-DAPv2. 2021 compared to GLODAPv2. 2020. This is due to the removal of data with missing temperatures (Sect. 3. 3. 1). Figure  9  illustrates the seasonal distribution of the data. As for previous versions there is a bias around summertime in the data in both hemispheres; most data are collected during April through November in the Northern Hemisphere, while most data are collected during November through April in the Southern Hemisphere. These tendencies are strongest for the poleward regions and reflect the harsh conditions during winter months which make fieldwork difficult.
The data are provided in two ways: as a set of individual exchange-formatted original cruise data files with assigned WOCE flags and as globally and regionally merged data product files with adjustments applied to the data according to the outcome of the consistency analyses. Importantly, no adjustments were applied to data in the individual cruise files, while primary-QC changes were applied. The consistency analyses were conducted by comparing the data from the 43 new cruises to the previous data product GLODAPv2. 2020. Adjustments were only applied when the offsets were believed to reflect biases relative to the earlier data product release related to measurement calibration and/or data handling practices, and not to natural variability or anthropogenic trends. The adjustment table at https: //glodapv2-2021.
geomar. de/ (last access: 29 June 2021) lists all applied adjustments and provides a brief justification for each. The consistency analyses rely on deep ocean data (> 1500 or 2000 dbar depending on region), but supplementary CANYON-B and CONTENT analyses consider data below 500 dbar. Data consistency for cruises with exclusively shallow sampling was not examined. All new pH data for this version were comprehensively reviewed using crossover analysis, and none required adjustment. Regardless, full reanalysis of all available pH data, particularly in the North Pacific, will be conducted for GLODAPv3. Secondary QC flags are included for the 12 core variables in the product files. These flags indicate whether (1) or not (0) the data successfully received secondary QC. A secondary QC flag of 0 does not by itself imply that the data are of lower quality than those with a flag of 1.
It means these data have not been as thoroughly checked. For δ 13 C, the QC results by  Becker et al. (2016)  for the North Atlantic were applied, and a secondary QC flag was therefore added to this variable. The primary WOCE QC flags in the product files are simplified (e. g. , all questionable and bad data were removed). For salinity, oxygen, and the nutrients, any data flagged 0 are interpolated rather than measured. For TCO 2 , TAlk, pH, and fCO 2 any data flags of 0 indicate that the values were calculated from two other measured seawater CO 2 variables. Finally, while questionable (WOCE flag = 3) and bad (WOCE flag = 4) data have been excluded from the product files, some may have gone unnoticed through our analyses. Users are encouraged to report on any data that appear suspicious. Based on the initial minimum adjustment limits and the improvement of the consistency resulting from the adjustments (Table  7 ), the data subjected to consistency analyses are believed to be consistent to better than 0.
005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg -1 in TCO 2 , 4 µmol kg -1 in TAlk, and 5 % for the halogenated transient tracers. For pH, the consistency among all data is estimated as 0. 01-0. 02, depending on region. As mentioned above, the included fCO 2 data have not been subjected to quality control; therefore no consistency estimate is given for this variable. This should be conducted in future efforts.
Improvements resulting from quality control of the 43 new cruises per basin and for the global dataset. The values in the table are the weighted mean of the absolute offset of unadjusted and adjusted data versus GLODAPv2. 2020. The total number of valid crossovers in the global ocean for the variable in question is n. The values in this table represent the inter-cruise consistency in the GLODAPv2. 2021 product.
Processing of Trajectories The tracking data obtained for the different species and the robotic fish encodes the position x i (t) [x i (t), y i (t)] T of the individual i for each frame t. We approximate the velocity of each individual from subsequent positions by computing: v i,x (t + Δt) x i (t + Δt) -x i (t) Δt. (1) We can approximate the direction of motion of individual i by φ i (t) arctan2(v i,y (t), v i,x (t)). Similar to Eq. 1, we compute the angular speed _ φ(t) of each individual.
Single Collective   (E-H ). For mollies we also show the relation between turning rate speed of individuals swimming in groups of N 4 (inset G). The parameters of the model simulations (described in Section 2. 5) are listed in Table  2 ; Supplementary Table  S5  with the difference of a turn friction of α 0. 1. The parameters of the model fits in Supplementary Table  S4. preferred speed v 0 1 [L/T ] group size N 400 [1] speed relaxation β 0. 2 [1/T ] alignment strength μ alg 2 [1/T ] turn friction α 1 [L/T ] distance strength μ d 2 [L/T 2 ] angular noise D φ 1 [L 2 /T 3 ] distance slope m d 2 [1/L] velocity noise D v 0. 4 [L 2 /T 3 ] preferred distance r d 1 [L] We find for all cases a speed distribution that shows a strong variation in speed (Figures  2A-D ). The Coefficient of variation (COV(v) σ v /〈v〉) of individual speed for the different datasets is: COV(5) 0.
92 ± 28 (RoboFish), 0. 57 ± 0. 1 (guppy), 0. 92 ± 0. 78 (single molly), 0. 68 ± 0. 03 (model). The model-fit to explain the four datasets was significantly (ΔAIC ∈  [31, 85] , ΔBIC ∈ [28, 81]) improved by taking turning friction into account (Figures  2E-H ; Table  3 ; same for the group molly data: inset Figure  2G ).