dataesr/scientific-paragraphs-categorization huggingface dataset:
Addressing this need, this paper presents stable nitrogen isotope data from a WRZ rock hyrax midden, collected from the Groenfontein Private Nature Reserve in the Swartruggens Mountains. Extending from 1600 cal yr BP to an estimated 70,000 cal yr BP, the record provides a unique long-term perspective on local/sub-regional hydroclimates across MIS 1-4, with MIS 2-4 being particularly well-resolved. This record is significantly longer than those previously available from the region (~19,000 cal yrs BP from De Rif  (Chase et al. , 2015a; Chase et al. , 2011)  and ~23,000 cal yrs BP from Pakhuis Pass  (Chase et al. , 2019a; Scott and Woodborne, 2007b) ), providing new insights into WRZ palaeoclimate during this period. These data also enable a more complete assessment of inter-regional climate dynamics during the last glacial period through comparison with the longer records available from the SRZ  (Partridge et al.
, 2016). The radiocarbon ages were corrected for isotope fractionation using the AMS measured δ 13 C. The ages were calibrated using the SHCal20 calibration data  (Hogg et al. , 2020). The rbacon v. 2. 5. 8 software package  (Blaauw and Christen, 2011; Blaauw et al. , 2020)  was used to generate the age-depth model (Fig. 4 ).
7 ). A direct influence of expanded tropical easterly flow could be inferred from this relationship, resulting in more summer rain at Groenfontein, and a shorter/less intense dry season. This may be supported by speleothem δ 13 C data from the Cango Caves region of the southern Cape, with higher δ 13 C values at 30. 5 ka and 55 ka perhaps reflecting increases in warm growing season C 4 grasses at times of increased humidity at Groenfontein. However, factors associated with climate variability at Groenfontein may not simply relate to changes in the strength of moisture-bearing systems, but also to those aspects of the regional climate system that determine their position and extent. From geologic to centennial timescales, the existence and dynamics of the WRZ have been shown to be intrinsically associated with coastal upwelling in the Benguela system, with global cooling intensifying the strength of the South Atlantic Anticyclone and the upwelling of cold water along the coast  (Etourneau et al.
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ),  ERC Starting Grant "HYRAX" , grant agreement no. 258657. The authors would like to thank  Nicholas Wilshire  for contacting us regarding the hyrax midden analysed for this study and  Volker Miros  and the owners of the  Groenfontein Private Nature Reserve  for allowing us to access to their land to collect midden material.
Alternatively, particle extinction and optical depth, dominated by larger particles, are a common proxy for CCN concentrations  19, [22] [23] [24]. The assumption based on particle size determining CCN activity of particles is based on the Kelvin effect, which describes the reduction in saturation vapor pressure due to the reduced curvature associated with larger particles  [25] [26] [27]. The datasets presented here include both CCN measurements and metrics based on particle size distributions for comparisons with remote retrievals based on particle size. Remotely retrieving aerosol properties in the presence of clouds is not currently possible. In cloudy regions, the gaps in CCN data can be filled with remote retrievals of cloud droplet number concentration (CDNC), representing the CCN concentration at the supersaturation level reached in the cloud  14, 28, 29.
Aerosol cloud meteorology interactions over the western atlantic experiment (ACTIVATE). ACTIVATE took place off the coast of the United States and was based out of NASA Langley Research Center for over three years and six intensive aircraft campaigns to measure aerosol and cloud properties and robustly characterize aerosol-cloud-meteorology interactions. Instrumentation was deployed on two aircraft, the King Air and Falcon, for simultaneous remote and in-situ measurements by the two aircraft, respectively. The scientific goals of ACTIVATE are (1) to improve understanding and model representation of relationships between aerosol number Table  1. Campaign datasets used for the proposed study along with information on their locations, and typical aerosol, and cloud regimes.
Clouds aerosols precipitation radiation and atmospheric composition over the southerrn ocean (CAPRICORN2). CAPRICORN2 consisted of ship-based measurements on the R/V Investigator. Measurements were conducted south of Tasmania, Australia (Fig. Instrumentation. Tables  2, 3  summarize the measurements made on each airborne and ship campaign, respectively. The available instrumentation listed in these tables is not exhaustive. This manuscript contains only CDNC, CCN, CCN proxies, and measurements necessary to identify particle physical and chemical properties and non-marine contributions to particle concentrations. Other measurements not discussed here are accessible through the original campaign archives referenced in the Data Records section. This section provides specific information for the listed instrument.
The longer sampling time for ship measurements reduces the uncertainty relative to the aircraft measurements  50. Although the AMS collection efficiency varies by instrument and with atmospheric conditions and is sometimes not corrected for or assumed in the published datasets, the non-refractory particle composition ratio is still a relevant and valuable quantity for evaluating bulk particle chemical properties. On aircraft, during NAAMES, ACTIVATE, and CAMP 2 EX, the AMS measures particle mass concentrations in V-mode (high sensitivity) at 30 second intervals. During the NAAMES campaigns, the AMS on the R/V Atlantis had much longer sample cycle times. Each sample cycle lasts a total of 5 minutes. During each cycle, the ambient aerosol is sampled in V-mode (high sensitivity) for 2 min, W-mode (high resolution) for 1 min, and event-trigger-mode (single particle composition) for 2 min.
8 g cm  -3. The SP2 sampling rate on the NAAMES ship and all aircraft campaigns are 60 s and 10 s, respectively. The SP2 instruments used in these studies are calibrated to similar but slightly different diameter ranges, reported in the originally published campaign datafiles. Black carbon mass outside the calibrated range is still accounted for using a log-normal fit to the distributions. For SP2 measurements collected in-flight, where sampling rates are increased due to the considerable distance covered by the aircraft, the log-normal fit is applied to the flight-averaged data to produce a correction coefficient that is applied to the entire flight. The correction coefficient typically increases the black carbon mass by less than 10%. During the NAAMES and ORACLES aircraft campaigns and MARCUS ship campaigns, the carbon monoxide mixing ratio is measured with a CO/CO 2 gas analyzer (Los Gatos Research, San Jose, CA).
5-50 µm in diameter. Both the CDP and FCDP are sensitive to some coarse mode aerosol. The Fast Forward Scattering Spectrometer Probe (FFSSP, SPEC inc. , Boulder, CO) also measures particles from 1. 5 to 50 µm in diameter. The Cloud-Aerosol Spectrometer (CAS, DMT, Boulder, CO) measures particles ranging from 0. 51-50 µm in diameter at ambient relative humidity. The CDP, FCDP, FFSSP, and CAS measure both liquid and ice particles and cannot distinguish between the two phases. The Cloud Imaging Probe (CIP, DMT, Boulder, CO) measures the size of particles from 25-1550 µm with 25 µm bins. The Two-Dimensional Stereo Optical Array Probe (2D-S, SPEC inc. , Boulder, CO) images cloud particles to obtain droplet sizes ranging from 25-1280 µm diameter range. For both the 2D-S and the CIP, the smallest bins (<50 µm) are excluded from the aggregated datasets due to the large uncertainty associated with them.
1 m s -1. Some probe data is excluded from this data descriptor after careful analysis. The Flight Probe Dual Range Phase Doppler Interferometer (FPDR-PDI, Artium Technologies Inc. , Sunnyvale, CA) was only used during the ORACLES campaign and is a redundant measurement as it overlaps entirely with the CDP and CAS size range. For this overlapping size range, Gupta et al. 54  determined that the CAS and CDP were the better measurements for 2016 and 2017-2018, respectively; therefore, the FPDR-PDI measurements are excluded. In addition, during SOCRATES, the Precipitation Imaging Probe (PIP, DMT, Boulder, CO) and a 2-Dimensional Cloud probe (2D-C) which measure large precipitation-sized particles, were both deemed unusable due to a problem with the time record and degraded image quality, respectively  55.
Data Records An aggregated dataset, consisting of time series with all in-situ aircraft or ship campaign measurements presented in Tables  2, 3 , is available through Dryad  66. All missing or invalid data flags are converted to 'Na'. Some datasets have already been filtered for inlet shattering in-cloud and measurement contamination from ship exhausts; however, methods of filtering ship exhaust vary by campaign. For the NAAMES ship campaigns, the research ship exhaust was identified and filtered out based on the wind direction relative to the ship exhaust and total particle counts. For CAPRICORN2, wind direction, total particle counts, black carbon particle concentration, and CO and CO 2 measurements were also utilized in filtering ship exhaust  67. Finally, the MARCUS ship exhaust contamination periods are identified and filtered using total particle counts and CO measurements  67.
The aggregated dataset is further filtered to eliminate measurements influenced by in-cloud inlet shattering and averaged at 10-second intervals for aircraft measurements and 5-minute intervals for ship measurements (except for CAPRICOR2, which is only publicly available at hourly averaged intervals). While the remote HSRL and RSP measurements are briefly discussed, they are not included in the aggregated dataset. Remote retrievals methods of aerosol, CCN, and CCN proxies are constantly being updated and improved. Therefore, the raw data may be necessary to validate methods developed in the future. The remote measurement can be found in each campaign's data archive, discussed in the last paragraph of this section. Evaluating current published remote retrieval methods is a focus of ongoing work.
In the discussed campaigns, many instruments measure particle, cloud, drizzle, and precipitation size distributions (discussed in the methods section and shown in Tables  2, 3 ). Almost all have different size range limitations. Here we create value-added products and size distinctions in the aggregated dataset to highlight two other variables related to CCN concentration and to create consistency between instrumentation products with varying limitations. First, we create a CCN proxy based on particle size from instruments that measure submicron particles. The proxy is calculated as the total number of particles > 0. 1 µm diameter. How well this proxy represents the CCN concentration depends on the particle composition and supersaturation (Fig. 4 ). In the aggregated dataset, this CCN proxy is identified by variable names that start with the instrument abbreviation and end with " > 0.
These cloud droplet measurements are identified by variable names that end with "<50" to denote that these variables represent cloud droplet measurements of droplets less than 50 µm diameter. Measurements of droplets greater than 50 µm in diameter are separately defined with variable names ending with ">50". These likely represent drizzle or precipitation-sized droplets and could help identify the impact of drizzle and precipitation on remote sensing retrievals estimating cloud properties. Specifically, the CIP and 2D-S are available in the aggregated dataset as total droplet concentrations greater than 50 µm diameter. If full-size distributions or different thresholds instead of the integrated value-added products, the data can be obtained from permanent archives. Measurements are retrieved from several data repositories to create the aggregated dataset.
The original data for the NASA-led NAAMES  70  , ACTIVATE  71  , ORACLES  72  , and CAMP 2 EX 73 are available through the Earthdata Atmospheric Science Data Center Sub-Orbital Order Tool (SOOT) (https:// asdc. larc. nasa. gov/soot/). SOOT will soon incorporate the ability to download multiple variables in a custom merge file. The CAPRICORN2 CPC, CCN, and ACSM measurements are available through the Commonwealth Scientific and Industrial Research Organization Data Access Portal  74. The remaining CAPRICORN2 data in the aggregated dataset have not been previously published. The measurements are archived under several single digital object identifiers (DOIs) for the SOCRATES and MARCUS campaigns. All the SOCRATES data is available through the NCAR/UCAR Earth Observatory Laboratory Data Archive, where the HSRL  75  , 2D-S  76  , CCN  77  and remaining data  78  are available via separate DOIs.
02 g m -3. Then a 10-second buffer is applied to remove cases at cloud edges (i. e. , measurements within 10 seconds of exiting or entering a cloud are excluded). Finally, a 10-second average is applied to the CDNC measurements to produce Fig. 2. The CDNC comparisons generally show good agreements (r ≥ 0. 70). The worst correlations were from the NAAMES airborne campaigns in comparisons with a CDP and a CAS because there were often problems with the CAS significantly undercounting the CDNC. For this reason, it is recommended that the CDP measurements are prioritized over the CAS for CDNC for the NAAMES dataset. Excluding the CDNC comparisons from NAAMES, the correlations are strong (r ≥ 0. 89). It is worth noting the FCDP may provide more accurate measurements than the CDP, CAS, or FFSSP in conditions where CDNC is high (>200 cm -3 ) due to the higher possibility of coincidence in the CDP, which leads to undercounting and over-sizing of cloud droplets  [86] [87] [88].
aerosol and CCN measurements. The in-situ CCN concentration is compared to total CN (Fig. 3 ) and CN greater than 0. 1 µm (CN >0. 1 ) (Fig. 4 ) for all aircraft and ship-based campaigns. For aircraft-based subsaturated measurements, TWC is used to exclude in-cloud measurements. For out-of-cloud measurements, the TWC is required to equal 0 g m -3 , a 10-second buffer is applied to remove measurements near the cloud that may be influenced by inlet shattering (i. e. , measurements within 10 seconds of exiting or entering a cloud are excluded), and a 10-second average is applied. Ship-based measurements are averaged at 5-minute intervals, except for the CAPRICORN2 dataset, which is provided at hourly averaged intervals. As expected, the comparison in Fig. 3  shows CCN concentration is less than or equal to the CN concentration (within the measurement error), with measurements at higher supersaturations matching more closely with the CN concentration than at lower supersaturations.
Usage Notes The measurements shown in this data descriptor and other in-situ measurements in Tables 2, 3 are processed from the publicly available data repositories using a customized python code. The python code is available for transparency; however, due to the large number of data files needed for input and the need to account for variable file formats, the output files with the time-synced datasets are available in '. csv' format. This aggregated dataset is compiled with the purpose of validating remote retrievals of CDNC, CCN, and CCN proxies from satellite, HSRL, and RSP data available at the time of the campaigns. The goal is to improve satellite retrievals and model representation of aerosol and cloud properties. Other possible uses of this dataset include: • Statistical studies quantifying the influence of regional pollutant perturbations and environmental differences on marine particle and cloud properties.
acknowledgements This work was supported by the NASA ROSES Atmospheric Composition Campaign Data Analysis and Modelling (ACCDAM) program, grant NNH20ZDA001N. We acknowledge the efforts of the entire SOCRATES, MARCUS, NAAMES, ORACLES, ACTIVATE, CAMP  2  EX and CAPRICORN2 teams in collecting the highquality datasets. We acknowledge Marc Mallet his work processing the CAPRICORN2 data. The CAPRICORN2 project received grant funding from the Australian Government. The Authors wish to thank the CSIRO Marine National Facility (MNF) for its support in the form of sea time on RV Investigator, support personnel, scientific equipment and data management. All data and samples acquired on the voyage are made publicly available in accordance with MNF Policy. Gourihar Kulkarni acknowledges support from the Office of Science of the US Department of Energy (DOE) as part of the Atmospheric System Research Program.
, St. Paul, MN) measures particle optical diameter distributions between 0. 1 µm and 3. 5 µm. The Aerodynamic Particle Sizer (APS Model 3321, TSI Inc. , St. Paul, MN) characterizes the aerodynamic diameter of large dry particles, including dust, and has a size range of 0. 5-20 µm; however, the inlet cut size is approximately 5 microns 46. On CAPRICORN2, the APS Model 3320 (TSI Inc. , St. Paul, MN) is used. The APS has 50 diameter bins. The long differential mobility analyzer (LDMA Model 3934, TSI Inc. , St. Paul, MN) measures aerosol size distributions in the 0. 01-0. 55 µm diameter range. During ORACLES in 2016, a leak affected the LDMA, which is accounted for with an altitude-dependent correction. Also, the 2016 and part of the 2017 datasets have been multiplied by a factor of 1. 6 to account for particle loss in a partially blocked 0.
Modern morphometric-based approaches provide valuable metrics to quantify and understand macroevolutionary and macroecological patterns and processes. Here we describe TriloMorph, an openly accessible database for morpho-geometric information of trilobites, together with a landmark acquisition protocol. In addition to morphological traits, the database contains contextual data on chronostratigraphic age, geographic location, taxonomic information and lithology of landmarked specimens. In this first version, the dataset has broad taxonomic and temporal coverage and comprises more than 55% of all trilobite genera and 85% of families recorded in the Paleobiology Database through the Devonian. We provide a release of geometric morphometric data of 277 specimens linked to published references. Additionally, we established a Github repository for constant input of morphometric data by multiple contributors and present R functions that help with data retrieval and analysis.
Several palaeontological studies have highlighted the critical role of trilobites in the signature of the Cambrian-Ordovician ecosystems and, although declined significantly during the Late Ordovician extinction, they continued being an emblematic group of the benthic ecosystems during the middle Palaeozoic  10. Palaeobiodiversity studies have classically examined taxonomic changes on the basis of the fossil record  [11] [12] [13] [14] [15]. Indeed, understanding the patterns of diversity collapses and recoveries provides valuable insights into drivers of changes and helps to inform conservation activities in modern marine ecosystems in light of anthropogenic climate change  16, 17. Most of these studies are facilitated by the Paleobiology Database  18  (PBDB) or the Geobiodiversity Database  19  (GBDB), which are comprehensive, international collaborative, taxonomic and occurrence databases already available and widely used to assess macroevolutionary patterns and processes in deep time and for global to regional analyses  [20] [21] [22] [23] [24].
Regarding morphological information, only small datasets linked to specific publications exist, while very few large scale morphological datasets are currently available (e. g. [38] [39] [40]  ). Regarding trilobites, there has been an increase in taxonomically comprehensive morphological databases  41  , such as outline data describing the shape of the cranidium  31  , semilandmarks describing the shape of the cephalon  42  , landmark and semilandmarks configuration on cephalic outline, glabellar and eye ridges  43  , landmarks and semilandmark data on cephala and pygidia  44  , as well as trilobite moult morphometric measurements  45. Here, we develop a database for morphological information of trilobites that is also quantified by means of geometric morphometrics state-of-the-art methods.
The last trilobites finally disappeared at the end-Permian mass extinction about 252 Ma ago  49. Trilobites were among the most successful of all early animals, thriving in oceans for almost 270 million years, with over 20,000 species having been described  41, 50. Due to their excellent fossil record and high diversity and abundance, they are an ideal group for analysing biotic changes during the Palaeozoic. In this first release of the openly accessible "TriloMorph" Database, we describe the general landmarking protocol and database functionality (Fig. 1 ). The initial dataset is focused on Devonian trilobites, and late Cambrian-Early Ordovician ones to a lesser extent, which have a very rich fossil record at times where major transitions of life happened  10, 51. The morphology of most Devonian trilobite genera with records in the PBDB has been digitized and compiled in the database which, in addition to these morphological traits, contains contextual data on chronostratigraphic age, geographic location, taxonomic information, and lithology of the landmarked specimens.
Landmark quantification may vary between specimens depending either on the preservation of the specimen or because the specific trait is not present in the respective taxon; for example eyes in blind taxa or specimens with no articulated cephala. In the case of an incomplete specimen selected for digitization, for example, a cephalon with a broken genal spine, we completed its morphology by taking in reference another specimen from the same publication. In exceptional cases where certain taxa do not have well preserved structures, drawings were used for digitization if they were based on a specific specimen with a repository code. However, it is frequent that some taxa lack certain structures; in these cases, we suggest removing these traits from the analysis. In the static release, 82% of the specimens in the database allow complete landmark acquisition in the cephala and 100% in the pygidia.
Also, this first release includes only adults (early holaspid and holaspid) specimens. Landmark and semilandmark selection will ultimately depend on the scope of the analysis. Thereby, here we present a comprehensive digitization protocol. Several softwares exist for 2D landmark acquisition, such as 'ImageJ' 55 , 'tps' series  56  , or the digitize2d() function in the 'geomorph' R package  57, 58. Here, we used both the 'digitizeImages()' function in the 'StereoMorph' package  59  for R  60  , and the classical 'tpsDig' software  56. : specimen ID conforms the basic unit of entry in the main table that contains specimen-level traits and contextual characteristics ('data. csv'). (3) PBDB + TriloMorph: taxonomic hierarchical structure and stratigraphic ranges can be obtained by merging TriloMorph data with occurrence and higher taxonomic information from the PBDB by using the accepted name from PBDB and species or genus names from TriloMorph, (4) Generalised Procrustes analysis: standardisation of landmark data.