dataesr/scientific-paragraphs-categorization huggingface dataset:
Age modelling Whereas the initial steps of data discovery and synthesis could rely on published chronology, analysis of the complete dataset requires the development of a common chronological framework. This framework must be constructed in a way that not only allows for a consistent method of assignment of ages to depths within each core but also allows for the consistent and quantitative assessment of age uncertainty. To this end, we follow an approach that combines absolute ages (radiocarbon ages, tephra layers and palaeomagnetic events) with δ 18 O stratigraphy. As a result, our age models may differ from those reported in the original publication(s). This does not mean that the updated age models are better (constrained), but they are constructed in a way that allows for applicability and consistency across the synthesis.
To ensure a common chronological framework for all time series in the synthesis, radiocarbon ages were recalibrated using the IntCal13 curve  (Reimer et al. , 2013). Since reservoir ages vary in space as well as in time, we used reservoir age estimates based on a comprehensive ocean general circulation model  (Butzin et al. , 2017)  to account for this variability in a physically plausible way. To derive the reservoir age and uncertainty for a measured radiocarbon age, PDV (i) extracts all modelled radiocarbon ages from the nearest grid cell in the modelled dataset, (ii) finds all modelled radiocarbon ages that are possible within the error of the measured radiocarbon age, and (iii) takes the mean and the standard deviation of all corresponding reservoir ages to correct for the measured radiocarbon age.
Notes on palaeoclimate proxies in marine-sediment archives and metadata This synthesis contains climate-sensitive proxy data based on measurements using various biological sensors. It is not the intention here to provide a full overview of marine palaeoclimate proxies and their uncertainties (for this, see for example  Hillaire-Marcel and De Vernal, 2007; Moffa-Sánchez et al. , 2019) , but the fact that the proxies are based on biological sensors means that they are affected by different ecological bias in addition to observational noise. Basic knowledge of the recording system is therefore essential for the interpretation of the data and may aid in explaining differences be-tween proxies for the same climate parameter. These considerations were also essential to choosing the range of metadata to be recorded alongside each palaeoclimatic parameter to allow for a proxy-specific assessment of uncertainty.
For all proxies based on foraminifera, it is relevant to record the species as well as the number of individuals that were pooled for geochemical analysis. The latter is because the short lifespan and variable habitat of foraminifera species cause large variability among individuals. Planktonic foraminifera shell size may for several reasons also affect their chemistry  (Jonkers et al. , 2013; Friedrich et al. , 2012)  as well as their assemblage composition  (Al-Sabouni et al. , 2007). Therefore, the size fraction of the analysed shells was included in the metadata whenever this information was available. Besides planktonic foraminifera Mg/Ca ratios, the U K 37 unsaturation index can provide information about near-seasurface temperature. The U K 37 index is based on the relative degree of unsaturation of C 37 alkenones, which is linearly related to temperature  (Prahl et al.
Structure of the database Following the data synthesis strategy outlined above, we generated a first data product for time series of eight parameters in sediment cores with radiocarbon and benthic δ 18 O stratigraphy. Following the logic of our approach, the synthesis is organised by the physical object from which the records were extracted (cores), here called site, to account for the inclusion of records from spliced cores. Each site in the data product has information on seven different themes (Fig. 2 ): 1. Geographic data contain the site name, latitude and longitude (in decimal degrees N and E), elevation or water depth (in metres), and possible notes that are relevant to the site or core as a whole. All fields except notes are essential and always included. 2. Metadata include the original parameter name as given in the online data file, a standardised parameter name, parameter type (measured or inferred), unit, an estimate of analytical error as determined from repeat measurements of a standard and an estimate of reproducibility as determined by repeat measurements on samples.
Original age models are preserved alongside the data time series as they may differ for different time series from the same site. The data also contain information on the sample number or label (mainly for DSDP, ODP and IODP cores) for spliced records and sample-specific notes. sites. An overview of all datasets used in this synthesis, including URLs to the data, can be found at https://doi. org/10. 5281/zenodo. 3739019 (Jonkers et al. , 2020). Data sources for each site are listed in the Appendix. By design all sites have both benthic stable-oxygen-isotope and radiocarbon data. The majority of the sites are close to the continents and in the Northern Hemisphere, with a concentration in the North Atlantic Ocean (Fig. 3 ). This reflects both research attention as well as the challenges of obtaining sediment cores with high accumulation rates and wellpreserved foraminifera.
The effect of research focus is also visible in the temporal coverage of the time series, where every parameter is characterised by a clear maximum around the last glacial termination ca. 15 ka (Fig. 4 ). The median resolution of the time series varies by 2 orders of magnitude but is generally better than one sample per 1000 years and fairly similar among the different parameters (Fig. 5 ). The updated age models are based on chronological control points (tie points) from radiocarbon dating and absolutely dated layers (using tephra and/or palaeomagnetic event stratigraphy) as well as alignment to the regional benthic δ 18 O stacks. The majority of the time series has a chronological control point at least every 5000 years (Fig. 6 ). Taken together, the coverage in space, time and across parameters indicates that the PalMod marine palaeoclimate data product allows for analysis of palaeoclimate on a supra-regional scale over the entire 130 000-year time frame.
Data formats We provide the data products in three different formats in order to facilitate access and analysis using different software and across operating systems. Given the structure of the formats, each representation is slightly different in its level of metadata detail and the way metadata and data are stored. The differences are described below. -Since the data product was built using R, the data product is presented in R-readable RDS files that contain for each site a list with data for each theme (Sect. 4). This is the format that is most complete, yet in the interest of memory space it preserves a random selection of 1000 age models from the larger ensemble produced using Bacon. In this format, all data and metadata for each site are contained within a single file. Example scripts (https://github.
com/lukasjonkers/PALMODutils, last access: 6 May 2020) allow the user to extract a quick overview of the contents of the data product with information on the temporal range, resolution and age control of the time series. Additional code is available to query the data product by parameter, parameter detail, sensor species, temporal range, resolution and age control. -The data product is also provided in the LiPD format, which is built around JSON-LD and CSV formats and is widely readable across different platforms. As with RDS, all data for each site are presented in a single file. Utilities to interact with LiPD files in R, Matlab and Python are available at https://github. com/nickmckay/ LiPD-utilities (last access: 6 May 2020). -Finally, the data are also provided in netCDF format in a way that allows reading with PDV.
This means that a single site has separate files for each individual palaeoclimate parameter as well as for the age model. In this format, some metadata are stored as concatenated strings rather than easily searchable attributes. The netCDF format allows however for the storage of the full suite of age model ensembles without excessive file sizes. The PDV software to read and process the data can be downloaded at https://www. marum. de/en/Stefan-Mulitza/PaleoDataView. html (last access: 6 May 2020). For all parameters the median resolution is more than one data point per 1000 years.
Future plans and versioning To increase the spatio-temporal coverage over the entire 130 000-year time frame of the database, updates of this data product will first aim for quantitative growth of the database by adding more time series with chronological control based on benthic foraminifera δ 18 O and absolute age control points other than 14 C. If available, these updates will also include the parameters listed in Table  1. They will be named using the counter following the first decimal separator. The structure of the data product is designed to be flexible, allowing for the addition of different metadata fields and parameters. Further updates that include new parameters and/or require a new age modelling approach (i. e. no benthic δ 18 O alignment) will be named using the counter before the first decimal separator.
Any updates to add or correct (meta)data to an existing version that do not increase the number of sites will be indicated using a counter following the second decimal separator. Future versions will be made available on PANGAEA, and links to the updates will be provided at https://doi. org/10. 1594/PANGAEA. 908831 (Jonkers et al. , 2019). also cite the primary source of the data when using (individual time series of) this product.
g. reflecting differences in the labelling during an expedition and in the repository. This naming confusion renders it difficult to combine datasets from the same core, especially in an automated way, and to assess the uniqueness of time series from the same core for dereplication. We recommend using the full name as indicated in the cruise report where the core was first described. Standardise vocabularies. Even though a vast number of palaeoclimate data are available in public repositories, the lack of standardisation of parameter vocabularies hinders efficient data processing. This problem is clearly illustrated by the fact that, for this synthesis, long synonym lists needed to be generated in order to group parameters. Each parameter in this synthesis had tens to hundreds of different names, of which many were unique (Fig.
However, this information is often not included alongside the digitally available data, and for this synthesis a large proportion of the radiocarbon (meta)data had to be scraped from the literature. In our age modelling approach, we took reservoir age uncertainty into account, using data derived from the modelled reservoir ages (see Sect. 2. 3). Alternative approaches are hindered by the fact that reservoir age uncertainty is almost never reported. Avoid redundancy. A considerable amount of time was spent on the dereplication of time series of the same parameter from the same core that were archived multiple times. Repeat archiving happens when data are reused or, less commonly, updated. The dereplication task is not made easier by (incomplete or inconsistent) metadata reporting and can be avoided through better linking of existing datasets when, instead of re-uploading the data, the DOI or URL of the original data is provided.
Help rescue dark data. This synthesis is based on data that are publicly available, yet many palaeoceanographic time series are not archived in public repositories. Even though the proportion of this so-called dark data is by definition not known exactly, it likely affects every branch of palaeoceanography, and as a result palaeoceanographical data syn-theses cannot be exhaustive. This problem is clearly exacerbated for syntheses relying on automated data mining. There are several shades of dark data, each requiring their own approach to retrieve them and make them available. Some data are only partially available, for instance datasets that lack sample depths. Such data only require additional data to make them reusable. These additional data can sometimes be calculated or obtained from cross-referencing different data files but in many cases will need to be retrieved from the data producers.
Other datasets, in particular those from before the digital age, are presented in tables in the original publications. Progress has been made with digitising those datasets (especially in PANGAEA), but this work is not finished, and more effort is needed to make this data available to the community. There are also data that are used in publications but are not made available in any way (print or digital). This third shade of data can so far only be obtained from the original data producers or authors of the original publication or, if this proves impossible, needs to be digitised from graphs. Digitisation inevitably leads to a loss of accuracy of the data, and a dataset retrieved in this way should be flagged. A final category of dark data consists of data that are not part of a publication.
Such datasets can be made publicly available and be associated with a DOI to ensure traceability. To reward data sharing, the use of data citations needs to be encouraged and data citations should be included in the evaluation of a researcher's impact. Shackleton and Pisias (2013),  Lyle et al. (2002) ,  Bond (1997 Bond ( , 1976 ) V23_81  Jansen and Veum (1990) ,  Broecker et al. (1988)   ). We thank colleagues in working group 3 from PalMod for discussion on data and metadata requirements and Rangelys Sorrentino for help with data processing. We also thank Blanca Ausin and the anonymous reviewer for their constructive feedback on an earlier version of this manuscript.
Gardner et al. (1997)enast et al. (2002),Gardner et al. (1997), Lund and Mix (1998) WIND_28K McCave et al. (2005), Johnstone et al. (2014), Kiefer et al. (2006) Y69_106 Lyle et al. (2002) Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-12-1053-2020-supplement. Author contributions. LJ, MK and SM conceptualised the study; LJ, OC and MK developed the workflow; LJ designed the database structure with help from OC and MK; LJ and AS compiled (meta)data; LJ carried out quality control; LJ updated age-depth models; LJ, SM and ML ensured integration with the PDV format; LJ and NM ensured the integration with the LiPD format; MK and SM acquired funding; LJ and MK wrote the manuscript, and all authors reviewed and commented on the manuscript.
Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Paleoclimate data synthesis and analysis of associated uncertainty (BG/CP/ESSD inter-journal SI)". It is not associated with a conference. Acknowledgements. This research was supported by PalMod, the German palaeoclimate modelling initiative (http://www. palmod. de, last access: 6 May 2020). PalMod is part of the Research for Sustainable Development initiative (FONA; http://www. fona. de, last access: 6 May 2020) funded by the German Federal Ministry of Education and Research (BMBF.
Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Paleoclimate data synthesis and analysis of associated uncertainty (BG/CP/ESSD inter-journal SI)". It is not associated with a conference. Acknowledgements. This research was supported by PalMod, the German palaeoclimate modelling initiative (http://www. palmod. de, last access: 6 May 2020). PalMod is part of the Research for Sustainable Development initiative (FONA; http://www. fona. de, last access: 6 May 2020) funded by the German Federal Ministry of Education and Research (BMBF , CLIMAP project members (1981) V24_253 Oppo and Horowitz (2000) V25_59 Sarnthein et al. (1988), Waelbroeck et al. (1998), CLIMAP project members (1981) V28_14 CLIMAP project members (1981), Kellogg et al.
Data availabilityThe PalMod 130k marine palaeoclimate data product can be downloaded in R, LiPD and netCDF format at https://doi. org/10. 1594/PANGAEA. 908831 (Jonkers et al. , 2019). The data can also be visualised and downloaded in LiPD and CSV formats at http://lipdverse. org/PalMod/ current_version/. We encourage users of the data product to www. earth-syst-sci-data. net/12/1053/2020/ Earth Syst. Sci. Data, 12, 1053-1081, 2020.
Biological sample imaging Wide-field fluorescence imaging: TIRF imaging on neuronal sample was done at 200 ms integration times and a low 30 mW laser power. Samples consisted in β2-spectrin labelled with AF647. Output angle was adjusted with a translation stage  64  until penetration depth was roughly around 200nm. STORM imaging: STORM imaging on COS-7 cells (microtubules and clathrin) and neurons (ß2-spectrin) was performed at 50ms exposure time using a HiLo illumination configuration. A STORM buffer (Abbelight Smart kit) was used to induce most of the molecules in a dark state. The sample was lit with laser powers of approximately 250 mW in the objective BFP and a scanned gaussian beam of σ=17 µm. Except for clathrin, all data acquisition was excited with an ASTER excitation scanning ten lines in 25 ms and a gap of 1.
4σ. For STORM on clathrin, the labeling was dense and blinking was slightly optimized by reducing the excited FOV to 140 µm x 140 µm, scanning eight lines in 25ms with a gap of 1. 2σ. The acquisitions were performed and analyzed using the Nemo software (Abbelight). Localization consisted in a wavelet segmentation after median background removal, followed by gaussian fits of individual point spread functions. Sample was drift-corrected using a classical redundant cross-correlation algorithm.
Image acquisition, processing and analysis Neo-Live (Abbelight) was used for image acquisition. Single molecule analysis was performed either with a homemade Python 3. 7 code or with Neo-Analysis (Abbelight). Data processing such as analysis and measurement of beads radius from (Fig. 2 ), nanoruler from (Fig. 3 ) and clathrin from (Fig. 5 ) was performed in Python; whose code is available online. Beads: Beads (microspheres) were detected on the TIRF image: we first applied a Laplace filter from SciPy library, followed by low-pass filtering in Fourier space to diminish noise. Use of an intensity threshold then proved sufficient to efficiently detect individual beads. Peaks positions were measured via local extrema algorithms. Nanorulers: Nanorulers analysis focused on resulting X,Y coordinates.
Acknowledgements We acknowledge people who provided valuable advice and support in the course of this study; particularly  P. Jouchet  and  C. Cabriel. We thank  S. Vassilopoulos  for discussions about clathrin organization in cells. M. Bardou  and  F. Boroni-Rueda  helped with cell culture and labelling. C. Hubert  and  E. Fort  provided the lasers and galvanometer scanners.
A computer-aided methodology for the approximate prediction of axially textured polycrystals' properties is presented. The input data for the developed application consist of: (a) the two-dimensional diffraction pattern of the material under investigation and (b) the tensors of the elasto-piezo-dielectric properties of the single crystal case. Program ANAELU 2. 0 allows the determination of the fiber axis inverse pole figure by means of a Rietveldtype procedure. The Material Properties Open Database (MPOD, http://mpod. cimav. edu. mx) provides free access to the experimentally determined values of the tensor properties for several crystal species. Practical estimates of polycrystals' properties may be obtained by averaging single crystals' properties tensors, with the orientation distribution function (the symmetry-axis inverse pole figure, in fiber textures) as a weight factor.
r e s u m e n Se presenta una metodología asistida por computadora para la predicción aproximada de las propiedades de policristales con textura axial. Los datos de entrada para la aplicación desarrollada consisten en: a) el patrón de difracción bidimensional del material bajo investigación y b) los tensores de las propiedades elasto-piezo-dieléctricas para el caso del monocristal. El programa ANAELU 2. 0 permite determinar la figura inversa de polos del eje de la fibra mediante un procedimiento de tipo Rietveld. La base de datos de propiedades de los materiales Material Properties Open Database (MPOD, http://mpod. cimav. edu. mx) proporciona acceso gratuito a los valores experimentales de las propiedades tensoriales de numerosas especies monocristalinas. Una metodología de valor práctico para el pronóstico aproximado de las propiedades de policristales texturados consiste en promediar los tensores de las propiedades de los cristales individuales, con la función de distribución de orientaciones (la figura inversa de polos del eje de simetría, en texturas de fibra) como factor de peso.
In the field of functional ceramics (bulk and thin films) the uniaxial compression and sputtering processes give rise to textures with axial symmetry ("fiber" textures). Some methods and programs, representative of the state of the art in diffractometric texture analysis, are described in the references  [15] [16] [17] [18] [19]. Texture analysis via two-dimensional (2D) Rietveld refinement is a novel trend in which our group has contributed the ANAELU program  [20]. This program has been used successfully in the characterization of diverse materials  [21] [22] [23]. Section 2 in this paper describes the characteristics of the current ANAELU version and presents its use in a representative case of functional ceramics. The polycrystal properties, in first approximation, are those of the single crystal, moderated by the texture.
Section 3 of this article presents a brief recap on single crystal properties. The MPOD database  [24, 25]  is introduced. The influence of texture on physical properties may be significant. Reference  [26]  describes the use of texture as a property optimization tool. Mechanical properties have been linked with texture for a long time and these studies are still of interest  [27] [28] [29]. In piezoelectricity, texture can lead to the improvement or disappearance of this response  [30] [31] [32]. Fiber texture is common in several processes and affects, among others; the optical and elastic properties  [33] , electric conductivity  [30] , magnetic properties  [34, 35]  and piezoelectricity  [36, 37]. Being able to predict approximately different properties, supported by prior knowledge of texture (axial case), can be of great help to save time and efforts in the laboratory.
2D diffraction. The ANAELU program The ANAELU program (ANAlytical Emulator Laue Utility)  [20, 41]  allows the diffractometric determination of the inverse pole figure (IPF) R y 0 (h)  [43]  associated to the symmetry axis y 0 of a polycrystalline sample with axial texture. In the mentioned case, the IPF of the symmetry axis is equivalent to the ODF. Taking as reference the experimental two-dimensional diffractogram of interest, the user proposes the crystalline structure, the fiber texture component and the estimated width of the orientation distribution. The relationship that links the angles of the figure is: I texture h (2Â, ˛) = I random h P h [ (Â, ˛)] (1) cos = cos Â • cos ˛ (2) The pole figures P h ( ) corresponding to the IPF considered are calculated by applying the fiber textures fundamental equation  [18] : P h = 1 2 2 0 R y (˚, « ) d« (3) Fig.