dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ). AT has much higher variability as a result of organic alkalinity contributions. Data on suspended matter and particulate carbon compounds have also been collected at the WCO during different times over the years. As shown in  (Rühl et al. , 2021) , the concentration of particulate organic carbon (POC) at a depth of 10 m at L4, measured between 2013 and 2017, is highly seasonally variable, but overall decreasing over the four-year period. It is unclear whether this is part of a more long-term cyclical pattern, or a true temporal trend in the data. Variability in particulate suspended matter concentration in general is less seasonal, and does not conform to any clear trend throughout the same time period  (Rühl et al. , 2021).
, 2023) , or pelagic habitats  (Ostle et al. , 2021; Holland et al. , 2023). One advantage of the WCO plankton data is that they are both relatively complete in terms of taxonomic resolution and that they span multiple decades, which has enabled their use as a testbed dataset for developing indicators  (McQuatters-Gollop et al. , 2019) , to examine how representative single sampling stations are of wider areas  (Ostle et al. , 2017)  and to develop indicators that include the full suite of plankton, including major groups such as gelatinous species and picoplankton, which are not included as indicators from other longer term monitoring programmes such as the Continuous Plankton Recorder. At wider ocean basin scales the Western Channel Observatory data contribute to a series of reporting networks, for example oceanography through ICES reports on ocean climate (IROC)  (Gonzalez-Pola et al.
The future: melding new technological developments with existing long time series Most of the longer time series data that we provide here have been collected with traditional techniques that require direct collection of samples, their transport to the laboratory followed by expert chemical or taxonomic analysis. These methods are expensive and time consuming and for this reason, time series worldwide are under threat from funding cuts and loss of APICS will generate abundance and diversity data for organisms spanning 3-4 orders of magnitude in size, i. e. , 5µm -20mm, on hourly time-scales, which will allow a ca. 100-fold increase in phyto-and zooplankton sampling frequency at Station L4 in 2024. APICS will allow critical plankton data to be collected at the same temporal resolution as physical and chemical variables.
, 2013; Parry et al. , 2021). These are complementary, rather than alternatives to ongoing monitoring and provide fresh views on how these ecosystems function. These new technologies provide far more data than can be processed manually and currently traditional methods are essential for ground-truthing the new data. We hope that sustained observations such as the WCO will embrace the strengths of both traditional and new approaches in the following decades. Citing the present paper when the data are used also allows standard literature searches to reveal data usage, and this provides valuable evidence to warrant continued funding of the WCO. In future, we aim to produce more WCO data papers with updated, doi'd time series, corrections of any errors and extended data fields. Importantly, some of the older (pre-1988) time series datasets and metadata held by the MBA were not available for this data paper.
Potential uses and limitations of these data The dataset we provide here has both strengths and limitations. Its main strength is that it combines, for the first time, data that span from oceanography to sharks and from microbial diversity up to benthic macrofauna and fish. We hope that this is particularly valuable for education purposes: for example student projects, where the student will spend less time trying to hunt down scattered datasets and melding them together, and more time analysing them. We have also, where we could, presented the data in units of mass as well as abundance since this is particularly amenable to carbon budgets  (Queirós et al. , 2019) , biogeochemical studies  (Barnes et al. , 2015)  or models  (Kenitz et al. , 2017; Polimene et al. , 2014). This study also represents the first attempt to put benthic and pelagic data sets together, and we hope this helps to make the WCO a natural laboratory to study benthic-pelagic coupling.
Another advantage of this summary version of the dataset is that it spans over 100 years, with over three decades of high-quality data from multiple trophic levels. Because the WCO has witnessed substantial warming and broadly responded in a manner similar to the wider NW European shelf  (Bedford et al. , 2020; Schmidt et al. , 2020)  our summary dataset provides a test-bed to study the mechanisms that control seasonality and climate change response across multiple trophic levels. Our data summary also has a series of key limitations, the first one being its taxonomic resolution. To make our database manageable in size, we have condensed large species lists (over 400 planktonic taxa alone) into a just a few dozen functional instance phenology shifts, bloom dynamics or extreme weather may prefer to access the individual timepoints which are typically weekly at L4.
Author contribution AJMc and AA co-ordinated the project. AJMc compiled the monthly database from the component datasets. AA, RA, RB, IB, EF, HF, CM, AJMc, CO, PS, TS, GT, KT, ST and CW submitted the individual datasets. AJMc and AA prepared the manuscript with contributions from HF, GT, CW, SR, RT, AQ, RA, RB, EF, TS, KT, CO. All authors are either current or previous producers of the component data sets.
Acknowledgements The work of the WCO is funded as part of the UK Natural Environment Research Council's National Capability and is delivered through the Climate Linked Atlantic Sector Science (CLASS) project. We are extremely grateful to the dedication of the ship's crews for sample collection and to the many analysts and taxonomic experts for sample processing. There would be no data without them. The Defra Pel-Cap project and the PML DataMap contributed to the completion of this datapaper. Thank you to Jon White for producing the WCO map (Fig. 1 ). Thank you to Emma Sullivan for providing Figure  2a , data sourced from European Space Agency (ESA) Climate Change Initiative (CCI) project and NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS).
Competing interests The authors have no competing interests Taken weekly where conditions allow. March 1988 to April 1993 surface temperature (0m) measured using a mercury thermometer in a stainless-steel bucket of freshly collected seawater. May 1993 to Dec 2001 a PML CTD was also used concurrently with the bucket method. Jan 2002 this PML CTD was replaced by a SeaBird SBE19+ CTD. Bucket temperatures were adjusted to CTD equivalents using a regression equation for parallel determinations. For surface values, we obtained a value for each sampling week based on this adjusted bucket temperature if only this was available. If both bucket and CTD data were available, we used the CTD temperature. We then derived arithmetic mean temperatures for each month. March 1988 to Dec 2021 monthly data derived from 1491 sampling points.
Publicly-accessible nutrient data accessed on 14 Jul 2022 from https://www. westernchannelobservatory. org. uk/data. php L4: Carbonate chemistry DIC (dissolved inorganic carbon) and TA (total alkalinity) L4_DIC_0m_µmol kg-1 L4_DIC_10m_µmol kg-1 L4_DIC_25m_µmol kg-1 L4_DIC_50m_µmol kg-1 L4_TA_0m_µmol kg-1 L4_TA_10m_µmol kg-1 L4_TA_25m_µmol kg-1 L4_TA_50m_µmol kg-1 Columns 27-34 Taken weekly where conditions allow. Borosilicate glass bottles with ground glass stoppers were used to collect seawater from the Niskin bottles. Sample bottles were rinsed, filled and poisoned with mercuric chloride according to standard procedures detailed in  Dickson et al. (2007). Samples were returned to PML for analysis. DIC was measured using a Dissolved Inorganic Carbon Analyser (Apollo SciTech, Model AS-C3).
Biomass of Copepod nauplii was calculated using the equations of  Uye et al (1996). , 1968, p. 153-157)  Both replicates samples are analysed by subsampling, enumerated and identified, currently using a Olympus SZX16 stereo microscope fitted with a SZX2-ILLT LED transmitted light illuminator stand. Source data represents weekly average abundance across the two replicates and converted to numbers in a m3. Monthly abundance represent an arithmetic mean value from between 1 and 5 visits in any given month and on a weekly basis. Biomass calculations derived from abundance data using a conversion factor of 0. 020375mcgC per cell using the equations of  (Menden-Deuer & Lessard, 2000). Be aware that zeros are present from 2009 onwards where there is confidence in the data. A zero represents looked for but not present in the sample analysed.
Data prior to this is less certain so zeros have been omitted. Although net design and methods of deployment have changed on several occasions, care has been taken to ensure that sampling characteristics have not altered appreciably. The 1m 2 Young Fish Trawl (YFT) fitted with a 700µm knitted mesh is hauled for 20 min in an oblique profile to an ideal depth of∼5m above the seabed. (Ostle et al. , 2021)  The samples are preserved in 4% buffered formalin and analysed as soon as possible after collection using a WILD M5 binocular microscope. The volume of filtered water is calculated using flow data recorded by a flowmeter fitted across the net mouth. Results are standardised to the number of individuals per 4000m3 in order to mitigate historical changes in sampling gear and deployment. A comprehensive summary of these macroplankton sampling methods and analysis is given in  Southward et al.
1903 data begins 1910 to 1920 no data 1939 to 1945 no data Surface data are most extensive. For each depth, number of sampling timepoints were 1146, 954, 892, 609, 740, 908, 262, and 815 respectively. Source dataset was produced for the ICES Report on Ocean by Tim Smyth https://ocean. ices. dk/core/iroc https://www. westernchannelobservatory. org. uk/data. php E1: Nutrients E1_Nitrite_0m_µm E1_Nitrite_10m_µm E1_Nitrite_20m_µm E1_Nitrite_30m_µm E1_Nitrite_40m_µm E1_Nitrite_60m_µm E1_Nitrite+Nitrate_0m_µm E1_Nitrite+Nitrate_10m_µm E1_Nitrite+Nitrate_20m_µm E1_Nitrite+Nitrate_30m_µm E1_Nitrite+Nitrate_40m_µm E1_Nitrite+Nitrate_60m_µm E1_Ammonia_0m_µm E1_Ammonia_10m_µm E1_Ammonia_20m_µm E1 Ammonia_30m_µm E1_Ammonia_40m_µm E1_Ammonia_60m_µm E1_Silicate_0m_µm E1_Silicate_10m_µm E1_Silicate_20m_µm E1_Silicate_30m_µm E1_Silicate_40m_µm E1_Silicate_60m_µm E1_Phosphate_0m_µm E1_Phosphate_10m_µm E1_Phosphate_20m_µm E1_Phosphate_30m_µm E1_Phosphate_40m_µm E1_Phosphate_60m_µm Columns 11-40 Taken fortnightly where conditions allow.
All depths sampled from 2018 Publicly-accessible nutrient data accessed from https://www. westernchannelobservatory. org. uk/data. php L4: Pigment sums generated from primary pigment data, determined by HPLC Taken weekly where conditions allow Parameter Names (if shortened versions used in column titles): Taken monthly where conditions allow. 4 or 5 replicate 0. 1m3 box cores of sediment collected from 50m depth. All sediment collected is sieved over a 0. 5mm mesh and retained fauna preserved in 10% formaldehyde solution. Source taxa are identified and counted using stereo and compound microscopy to species level or lowest possible taxonomic resolution. Abundance and blotted wet weight (0. 00000g) per taxa is recorded per 0. 1m3 box core sample. From 4 principle feeding traits, based on information primarily gathered from the BIOTIC database, 1 unique principle trait was assigned per taxa; calculated using algorithms based upon body composition, maximum length and body mass.
(   This summary data set provides a mean value of all available determinations within any given calendar month. In the original data set the symbol "<" refers to concentrations below detection limit. These have been assigned a value of zero before averaging. Taken fortnightly where conditions allow. Borosilicate glass bottles with ground glass stoppers were used to collect seawater from the Niskin bottles. Sample bottles were rinsed, filled and poisoned with mercuric chloride according to standard procedures detailed in  Dickson et al. (2007). Samples were returned to PML for analysis. DIC was measured using a Dissolved Inorganic Carbon Analyser (Apollo SciTech, Model AS-C3). The analyser adds a strong acid (10% H3PO4 plus 10% NaCl solution) causing carbon species within the seawater to be converted to CO2 gas, which is purged from the sample by pure nitrogen (N2) carrier gas, is dried and cooled to reduce water vapour.
https://doi. org/10. 5194/essd-2023-311 Preprint. Discussion started: 16 August 2023 c Author(s) 2023. CC BY 4. 0 License. 7. Data availability and how to cite themThe full data and metadata are stored in a reputable UK repository known as DASSH (The Data Archive for Marine Species and Habitats) at the Marine Biological Association, Plymouth, https://www. dassh. ac. uk. The data are available via the link http://doi. org/10. 17031/645110fb81749. (McEvoy & Atkinson, 2023). On using this particular version, we kindly request to cite both the actual data citation (McEvoy & Atkinson, 2023)  and to cite this paper in Earth System Science Data. This paper gives a full description of the methods and correctly citing it gives due credit to the authors who contributed the datasets.
database is a collaboration between the Shark Angling Club of Great Britain (SACGB) and the Sportfishing Club of the British Isles (SCBI). It is a collation of information records kept by the SACGB. Recreational angling trips from the port of Looe, Cornwall, within 10nm radius of E1. The data presented here are for years when monthly log-book information is currently available. The data record 64287 captures from 32906 trips from the port in 200 monthly periods between 1958 and 2021. Since 1998 all captures have been released. Data presented are the total number of captures in a given month, and the average catch per unit effort (as captures per trip).
There has, however, been a few further subdivisions to better reflect trophic mode. These functional group allocations are coded, and numbers-to-biomass conversion factors are provided within the "trait" header bar data from the source dataset. Therefore there are 8 functional groups based partly on size, taxonomy and trophic mode, and with separate columns for abundance and estimated biomass. Because biomass is a derived property, often with different conversion factors between the four seasons (see data source doi), it is best to use numerical abundance data for population dynamics studies and biomass data for models, carbon budgets etc. As previously stated the groups comprise the whole of the consistently identified zooplankton so adding them will give a good estimate of total metazoan zooplankton with the exception of Ctenophores (see below).
Single-cell spatially resolved proteomic or transcriptomic methods offer the opportunity to discover cell types interactions of biological or clinical importance. To extract relevant information from these data, we present mosna, a Python package to analyze spatially resolved experiments and discover patterns of cellular spatial organization. It includes the detection of preferential interactions between specific cell types and the discovery of cellular niches. We exemplify the proposed analysis pipeline on spatially resolved proteomic data from cancer patient samples annotated with clinical response to immunotherapy, and we show that mosna can identify a number of features describing cellular composition and spatial distribution that can provide biological hypotheses regarding factors that affect response to therapies.
Analyzing the complex datasets produced by these methods can be a daunting task, and extracting biologically or clinically relevant information remains a challenge. Here we present the Multi-Omics Spatial Networks Analysis library (mosna), which allows analyzing any type of spatially-resolved omic experiment to extract relevant cellular interactions or tissue spatial organization features relative to experimental or clinical groups. This library includes the computation of assortativity and mixing matrices, which quantify preferential interactions between cell subsets in terms of statistical estimates across the whole sample. mosna also implements the Neighbors Aggregation Statistics (NAS) method to discover local cellular niches by aggregating various features, such as cell types or raw markers data.
The most immediately available variables that come to mind to characterize samples or understand disease progression are often cell type proportions. Thus, mosna facilitates testing whether or not these variables are predictive of patients' status by computing p-values, corrected for false discovery rate with the Benjamini/Hochberg method by default. In the CODEX CTCL cohort, none of the cell type proportions had a statistically significant difference of distribution between responders and non-responders (Figure SI 1a), as noticed by the original authors of the paper describing this dataset, which results in a poor bi-clustering of patients and cell type proportions (Figure  SI 1b ). However, even if variables taken alone do not have a high predictive power, a combination of these variables could be predictive of patients' groups.
By repeating this process for all nodes, we obtain a NAS table with N C rows and N V xN S columns, where N C is the number of cells, N V the number of variables, and N S the number of statistics computed on aggregated variables. From this table, generally bigger than the original dataset, we perform dimensionality reduction and clustering (Figure  3c ). The resulting clusters are niches, defined either by proportions of cell types or by statistics on marker levels depending on the analysis choices. The niches can be visualized on the dimensionality reduced projection of the NAS data, and on the samples' spatial networks (Figure  3d ). Using the NAS method on the CODEX CTCL dataset, we could identify 15 niches defined from the spatial configuration of protein markers data. With mosna, one can easily look at the distribution of cell types across niches, normalized either by absolute proportions across samples, or by cell type or by niche (Figure  3e ).
Conclusion mosna leverages spatial network representation of tissue samples to extract biologically or clinically relevant features. It provides a set of methods to ease the analysis of complex datasets, with a pipeline starting from evaluating the predictive power of standard measures, such as cell types proportions, and computing more elaborated features such as preferential cell type interactions through the z-scored assortativity, and the discovery of local niches with the Neighbors Aggregation Statistics methods. The approach is compatible with all types of spatially-resolved omics experiments, in 2D and 3D, and can be applied on annotated cell types, raw markers data, or any pre-computed attribute per cell, such as morphology or any other phenotype. Spatial networks are commonly reconstructed using the centroids of detected cells, but thanks to tysserand, mosna can analyze networks reconstructed with segmentation masks, where edges represent observable direct contacts between cells, even at a distance from their centroid.
Cellular network reconstruction Spatial tissue networks are reconstructed with the library tysserand. For the CODEX CTCL dataset available at https://www. nature. com/articles/s41467-021-26974-6#Sec33, cells' centroid coordinates were available, networks were thus reconstructed with the Delaunay triangulation method after choosing by hand an edge trimming distance threshold of 200 pixels to have sufficient connectivity for all networks, but avoid connecting cells across what appeared as structural gaps, such as vasculature features. For each sample pre-treatment, a table of edges was produced, relating pairs of nodes ids, where nodes are cells and their id is given in the available data. All these edges tables were merged into a single one for access convenience for further analysis, together with their corresponding sample id to relate network data to patients.
Then, clustering is performed, using for example the Leiden library, which allows to select a clustering resolution, and the defined clusters correspond to cellular niches. Clusters can then be visualized on spatial networks, and proportions of cells in inches per patient can be used to train machine learning models to predict response to therapy. We should note here that it is strongly advised to choose an appropriate data transformation if the NAS method is used on marker levels, such as log+1 or CLR transformations for gene expression and protein content data respectively.
Machine learning model For now, mosna makes it easy to train a logistic regression classifier, penalized with ElasticNet, which is a combination of L1 and L2 regularization. The training is performed with 5 fold cross validation per default, and the best ratio of L1 and L2 regularization is optimized by grid search. In rare cases training can fail depending on how data is split. In this case training occurs with more training folds, up to 10 by default. Models' performance is assessed with the ROC-AUC, and plots of models' coefficients indicate the importance of variables and whether they are positively or negatively associated with response to therapy. In the near future other machine learning models will be wrapped for easy use within mosna.
In this work we introduce an MPO-based high-performance numerical solver for density matrix dynamics obeying the Lindblad master equation. The presented open-source package  [43] , called lindbladmpo, allows users to simulate the dynamics of (two-level) qubits, in a uniform rotating frame (i. e. , with time-independent coefficients in the master equation). General single-qubit Hamiltonian parameters are supported, together with two-qubit interactions [of "flip-flop" (XY) and Ising (ZZ) coupling form] with arbitrary connectivity, and three dissipative jump operators that describe energy exchange with a thermal bath and dephasing. The solver employs internally the open-source library ITensor  [44] , and simulations with tens to hundreds of qubits in 1D and 2D setups using an earlier prototype of this solver are reported in  [34, 35].
We discuss aspects important when undertaking such a systematic analysis, which is performed here in conjunction with the Python package qiskit-dynamics  [53, 54]  -employed for simulations with up to ten qubits. The source code used to generate the simulations and analysis presented in this work is included as an example in the solver repository  [43]. In Sec. 2 the model supported by the solver and its initial state and observables are specified. In Sec. 3 and Sec. 4 we present the results from the detailed numerical studies described above. In Sec. 5 we summarize the results and discuss our conclusions and potential outlook for further research. In the Appendix we present more details of the numerical study and the numerical verification of the results presented in this paper.
This can provide an important advantage over a brute-force approach that would scale exponentially in N. The solver is written in C++ and can take advantage of multithreaded implementations of the LAPACK linear algebra library. It exposes a command-line interface as well as an easyto-use Python wrapper with rich plotting capabilities. The solver uses the open source library ITensor  [44]  for the storage, time evolution and manipulation of the many-qubit state using MPS and MPO. The solver integrates the dynamics in fixed time steps using a Trotter expansion of order up to the fourth  [58] , adapted from the method W II of  [59] , that allows for a compact MPO representation of the evolution operator in the presence of nonlocal interactions (along the one-dimensional path used to encode the density matrix).