Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Inventory maps of hazardous geological processes_Transcarpathia, Ukraine

Under the ImProDiReT Project running at Regional Transcarpathia level an Inventory maps of the hazardous geological processes’ manifestations for the Transcarpathia (landslides, mudflows, flooding and flash floods, karst) have been created.

opencc-by-4.0Nov 2023View details →

Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes

Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively  (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers. For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a  confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations.  Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.

opencc-by-4.0Mar 2023View details →

CHEK To-be Digital Building Permit process map

To-be process map for digital building permit process as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit' (CHEK) https://chekdbp.eu  It is described in the CHEK project deliverable D1.1. This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No.101058559 The file is provided in - SVG format, open vector editable format; - Visio format, proprietary but editable format; - PDF format.

opencc-by-4.0May 2023View details →

Processing of MODIS-Aqua data with Self-Organizing Maps NeuroVaria method for the southern canary upwelling system

Abstract This ocean color dataset is derived from MODIS_Aqua sensor measurements covering the Southern Canary upwelling system. The raw L1A measurements were downloaded from NASA's Ocean Color web site and then processed using the Ocean Biology Processing Group's (OBPG) Multi-Sensor Level-1 to Level-2 (MSL12) code. The l2gen program, based on its standard process, generates Level-2 parameters consisting of the top of atmosphere radiance, the radiance of each ocean and atmosphere component, the measurement angles, Level-2 flags, ... The top of atmosphere radiance is pre-corrected to keep only a dependence on the diffuse transmittance, the aerosol contribution and the water leaving radiance. The pre-corrected product and measurement angles are assimilated using the Self-Organizing Map NeuroVaria (SOM-NV) code (Diouf et al., 2013). SOM-NV is an algorithm based on two statistical models that classify a dataset into a map, and then use the information from that map to deliver atmospheric and oceanic parameters from the satellite observation. The parameters of interest are the remote sensing reflectance spectra (Rrs(λ)) and the aerosol optical thickness (AOT) at 869 nm (aot_869). The Rrs at blue (443 and 488 nm) and green (547 nm) are used to calculate chlorophyll-a concentration from the OBPG OCx algorithm (chl_ocx, O'Reilly et al., 1998; Mobley et al., 2016). These geophysical parameters are projected onto a fixed grid at 1/96° resolution and archived in a daily netcdf format files. Each file contains five visible reflectances Rrs(λ) (with λ = 412, 443, 488, 531, and 547 nm), chl_ocx, aot_869, and latitude and longitude coordinates. These parameters are described in the files, along with the global attributes. The netcdf files are formatted as follows: SOM-NV-Ayyyydddhhmmss.nc; where yyyy = year; ddd = Julian day; hh = hour; mm = minute; ss = second. The extension "Ayyyydddhhmmss.nc", corresponds to the name of the MODIS_aqua file of the day. When two input files exist for the same day, within 5 minutes, the two scans are concatenated and the orbit keeps the name of the second file. All files are compressed internally to a size of 4, to facilitate transfers. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Résumé Ce jeu de données de couleur de l’eau est issu des mesures du capteur MODIS_Aqua sur la partie sud du système d’upwelling des Canaries. Les mesures brutes L1A ont été téléchargées du site Ocean Color de la NASA, puis traitées à l’aide du code de traitement « Multi-Sensor Level-1 to Level-2 (MSL12) » du groupe Ocean Biology Processing Group (OBPG). La version standard du programme l2gen génère les paramètres de niveau 2 constitués de la luminance totale mesurée, de la luminance de chaque composante du système océan-atmosphère, des angles de mesures, des masques de niveau 2, …. La luminance totale est pré-corrigée pour ne garder qu’une dépendance à la transmittance diffuse, à la contribution des aérosols et à la luminance marine. Le produit pré-corrigé et les angles de mesure sont assimilés à l’aide du code Self-Organizing Map NeuroVaria (SOM-NV) de Diouf et al. (2013). SOM-NV est un algorithme basé sur deux modèles statistiques qui permettent de classer un ensemble de données sur une carte, puis d’utiliser les informations de cette carte pour restituer les paramètres atmosphériques et océaniques de l’observation satellite. Les paramètres restitués sont les spectres de réflectance marine (Rrs(λ)) et l’épaisseur optique des aérosols (AOT) à 869 nm (aot_869). Les Rrs au bleu (443 et 488 nm) et au vert (547 nm) servent à calculer la concentration en chlorophylle-a à partir de l’algorithme OCx de OBPG (chl_ocx). Ces paramètres géophysiques sont projetés sur une grille fixe à 1/96° de résolution et archivés au format de fichiers netcdf journaliers. Chaque fichier netcdf contient cinq réflectances du visible Rrs(λ) (avec λ = 412, 443, 488, 531 et 547 nm), la chl_ocx, l’aot_869, et les coordonnées latitude et longitude. Ces paramètres sont décrits dans les fichiers, ainsi que les attributs globaux. Les fichiers netcdf sont formatés comme suite : SOM-NV-Ayyyydddhhmmss.nc ; avec yyyy = année ; ddd = jour julien ; hh = heure ; mm = minute ; ss = seconde. L'extension "Ayyyydddhhmmss.nc", correspond au nom du fichier MODIS_aqua du jour. Dans le cas où deux fichiers existent pour un même jour, à 5 minutes près, les deux scans sont concaténés et l'orbite garde le nom du deuxième fichier. Tous les fichiers sont compressés en interne à un niveau 4, pour faciliter le transfert.

opencc-by-4.0Sep 2022View details →

Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.

Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere

opencc-by-4.0Jun 2021View details →

Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping

This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model. The detail information on the products were given below: Name: DSMst-RothC 5-km global topsoil SOC stock products Period: 1981-2018 Spatial resolution: 0.041666667 degree Temporal resolution: 1 year CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic) Extent: -180°, -90°: 180°, 90° Data format: GeoTIFF Compression: LZW Data type: Float32 Unit: t C ha-1

opencc-by-4.0Jul 2021View details →

Process-structure-property map for organic solar cells

This archive contains: - 1708 morphologies generated using Cahn-Hilliard equation for two parameters: blend ratio in range (0.5-0.63) and chi/interaction parameter (2.3-4.0). Blend ratio and chi are the processing conditions. Morphologies are given in two formats: plt files (srcdata folder) - raw data from simulations, and txt file (data folder) in the row-wise format for Graspi. For quick visualization morphologies are visualized and stored in folder figs. The dataset contains: Five folders: - srcdata: the source data with all plt files (1708 files) generated by Cahn Hilliard equation solver - data: the data used by graspi to compute descriptors (these are txt files stored as row-wise array, the volume fraction has been segmented using tools of graspi) - logs: 1708 log files with the descriptors generated by graspi (native C++ version) - figs: 1708 png files with visualized morphologies Two tables (in comma separated values format): - Combined PSP.csv (combined data on process-structure-property maps), where process information consists of three variables: PHI, CHI, NN (volume fraction, interaction parameter and time step index) - AllPropertiesCurated.csv - File with results from EDD - see reference below for more details One shell script: extractDesc.sh to build CombinedPSP.csv table from three sources: filename (contains info about PHI, CHI, NN), descriptors (graspi and logs), and Jsc (from AllPropertiesCurated.csv) More details on the dataset: Wodo, O., J. Zola, . Pokuri, P. Du, and B. Ganapathysubramanian. "Automated, high throughput exploration of process–structure–property relationships using the mapreduce paradigm." Materials discovery 1 (2015): 21-28.

opencc-by-4.0Jan 2023View details →

Processed cadastre map for Aarhus municipality 2018

Cadastre map for Aarhus municipality The whole of Denmark is divided into cadastres, which represent legally binding property boundaries. The 2018 cadastre map comprises a total of 81,437 cadastre units within Aarhus municipality. The cadastre map contains some general land use information, such as whether a cadastre unit is part of the road or rail network and if it is primarily used for agricultural production. Furthermore, each cadastre unit contains a unique identification string, which can be linked to building specific information from the Danish Building and Dwelling (BBR) register and to ownership information from the Central Business (CVR) register. Based on this information, for the 2018 cadastre, we compiled information on ownership categories, building specific information (e.g. use, such as residential, business etc.) and whether a cadastre unit is publicly accessible. For some of the information, the cadastre map was overlaid with other spatially explicit datasets. The different columns in the compiled cadastre map are described in the table below Information contained in the 2018 cadastre map for Aarhus municipality <table> <tbody> <tr> <td> Column Name </td> <td> Description </td> <td> Unit </td> <td> Comment </td> </tr> <tr> <td> parcel_id </td> <td> Unique ID for cadastre parcel </td> <td> Number </td> <td> </td> </tr> <tr> <td> cvr_name </td> <td> Owner name from CVR (central business) register </td> <td> Text </td> <td> </td> </tr> <tr> <td> road_rail_code </td> <td> Code depicting if a cadastre is designated as road or railway line: 0 = Other; 1 = road; 2 = rail </td> <td> Number </td> <td> Based on information contained in the cadastre dataset </td> </tr> <tr> <td> road_rail_name </td> <td> Name for cadastres designated as roads or railways </td> <td> Text </td> <td> </td> </tr> <tr> <td> code_BBR </td> <td> Code depicting use of buildings from BBR (building and dwelling register) </td> <td> Number </td> <td> In case of more than one building use, the cadastre is assigned the building use with the largest area within the cadastre </td> </tr> <tr> <td> name_BBR </td> <td> Name for use of buildings from BBR (building and dwelling register) </td> <td> Text </td> <td> </td> </tr> <tr> <td> built_year </td> <td> Building year of oldest existing building on the cadastre </td> <td> Year </td> <td> </td> </tr> <tr> <td> ownership_code </td> <td> Code for type of ownership </td> <td> Number </td> <td> Based on re-classification of owner name from CVR-register </td> </tr> <tr> <td> ownership_name </td> <td> Name for type of ownership </td> <td> Text </td> <td> </td> </tr> <tr> <td> ownership_agg_code </td> <td> Code for aggregated type of ownership </td> <td> Number </td> <td> Based on re-classification of owner name from CVR-register </td> </tr> <tr> <td> ownership_agg_name </td> <td> Name for aggregated type of ownership </td> <td> Text </td> <td> </td> </tr> </tbody> </table>       <table> <tbody> <tr> <td> public_access_code </td> <td> Code depicting if a cadastre is publicly accessible: 0 = No public access; 1 = public access; 2 = road </td> <td> Number </td> <td> cadastres depicted as publicly accessible include: all publicly owned areas, unless they are farmed; common areas owned by private owner/housing associations; common areas owned by cooperative housing associations; private forests; privately owned nature areas, if these are not fenced </td> </tr> <tr> <td> public_access_name </td> <td> Name for public accessibility </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_01 </td> <td> Code for building use for 1st aggregation level </td> <td> Number </td> <td> Based on aggregation of use categories from BBR (building and dwelling register) </td> </tr> <tr> <td> build_name_01 </td> <td> Name for building use for 1st aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_02 </td> <td> Code for building use for 2nd aggregation level </td> <td> Number </td> <td> </td> </tr> <tr> <td> build_name_02 </td> <td> Name for building use for 2nd aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_03 </td> <td> Code for building use for 3rd aggregation level </td> <td> Number </td> <td> </td> </tr> <tr> <td> build_name_03 </td> <td> Name for building use for 3rd aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> public_access_greenspace_type_code </td> <td> Code for type of publicly accessible green space: 0 = No publicly accessible green-blue space; 1 = Institution; 2 = Common area; 3 = Cemetery; 4 = Sport/leisure; 5 = Other green-blue space </td> <td> Number </td> <td> Based on combination of ownership, building type and public accessibility </td> </tr> <tr> <td> public_access_greenspace_type_name </td> <td> Name for type of publicly accessible green space </td> <td> Text </td> <td> </td> </tr> <tr> <td> golf_course_code </td> <td> Code depicting if a cadastre is used as golf course: 0 = ND; 1 = golf course </td> <td> Number </td> <td> Based on owner name form CVR register </td> </tr> <tr> <td> golf_course_name </td> <td> Name for golf course </td> <td> Text </td> <td> </td> </tr> <tr> <td> riding_school_horse_racing_track_code </td> <td> Code depicting if a cadastre is used as riding school or horse racing track: 0 = ND; 1 = riding school/horse racing trach </td> <td> Number </td> <td> Based on owner name form CVR register </td> </tr> <tr> <td> riding_school_horse_racing_track_name </td> <td> Name for riding school/horse racing track </td> <td> Text </td> <td> </td> </tr> </tbody> </table>       <table> <tbody> <tr> <td> primary_school_code </td> <td> Code depicting if the cadastre belongs to a primary school: 0 = no primary school; 1 = primary school </td> <td> Number </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_type_code </td> <td> Code depicting school type: 0 = No primary school; 1 = public school; 2 = public special school; 3 = private school; 4 = private continuation school </td> <td> Number </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_type_name </td> <td> Name of school type </td> <td> Text </td> <td> </td> </tr> <tr> <td> primary_school_name </td> <td> Name of school </td> <td> Text </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_count_pupils </td> <td> Number of pupils attending school (2022) </td> <td> Number </td> <td> Based on school webpages </td> </tr> </tbody> </table>

opencc-by-4.0Jan 2021View details →

Process map for casting a long-term experimental campaign on RC shrinkage cracking.

This dataset presents the process map that was developed for the casting of a long-term experimental campaign on reinforced concrete (RC) slabs subjected to the combined effect of restrained shrinkage and vertical loads. This experimental campaign was performed in the scope of the FCT project "IntegraCrete: A comprehensive multi-physics and multi-scale approach to the combined effects of applied loads and thermal/shrinkage deformations in reinforced concrete structures''. The results from this experimental campaign are presented in Gomes et al (2020), while the conceptualization, planning and experimental procedures are described in detail in Gomes et al (2021). The latter is supported by this process map to describe the micromanagement plan that was devised for the casting day. 14 people were involved in the casting of 3 slabs and 2 complementary specimens inside a highly instrumented climatic chamber, as well as 38 specimens for concrete characterization at different ages and 12 load blocks to use as vertical loads. This process map was developed with the standard Business Process Model and Notation (BPMN).

opencc-by-4.0Feb 2022View details →

Organizing the fragmented landscape of multidisciplinary product development: A mapping of approaches, processes, methods and tools from the scientific literature - Searchable cartographies

This document gathers cartographies for the development of mechatronic products, cyber-physical systems and smart products. The three cartographies presented are associated with an open-access article – see the citation box below – and differ from the ones provided in the article in that they are searchable, which makes it easier to pinpoint references, concepts and techniques. This document comprises a legend, the cartographies and a list of associated references.  To contextualize the cartographies, the integration of digital and connectivity technologies in new products can invite companies to adapt their development. Organizing the fragmented landscape of multidisciplinary product development to help companies navigate the dense scientific literature corpus is a first step in supporting them in doing so. Multidisciplinary product development can be investigated by analyzing specific types of products that deal with both software and hardware development and can be referred to as cyber-physical systems, mechatronics, and smart products and systems in the literature. To support their development, 236 “concepts and techniques” (an expression that encompasses approaches, processes, methods and tools) were identified from 167 scientific papers through an extensive literature review and organized based on a four-level model paired with a decision tree. The mapping of the sorted concepts and techniques made it possible to generate graphical representations called “cartographies.” These cartographies represent a database of concepts and techniques for multidisciplinary product development and serve to support companies in their transformation from the product development perspective by providing them with a general overview of the related literature.

opencc-by-4.0Dec 2017View details →

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study

This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.

opencc-by-4.0Dec 2017View details →

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Process of systematic mapping

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Data from: Visualizing mineralization processes and fossil anatomy using synchronous synchrotron X-ray fluorescence and X-ray diffraction mapping

Fossils, including those that occasionally preserve decay-prone soft-tissues, are mostly made of minerals. Accessing their chemical composition provides unique insight into their past biology and/or the mechanisms by which they preserve, leading to a series of developments in chemical and elemental imaging. However, the mineral composition of fossils, particularly where soft-tissues are preserved, is often only inferred indirectly from elemental data, while X-ray diffraction that specifically provides phase identification received little attention. Here, we show the use of synchrotron radiation to generate not only X-ray fluorescence elemental maps of a fossil, but also mineralogical maps in transmission geometry using a two-dimensional area detector placed behind the fossil. This innovative approach was applied to millimetre-thick cross-sections prepared through three-dimensionally preserved fossils, as well as to compressed fossils. It identifies and maps mineral phases and their distribution at the microscale over centimetre-sized areas, benefitting from the elemental information collected synchronously, and further informs on texture (preferential orientation), crystallites size and local strain. Probing such crystallographic information is instrumental in defining mineralization sequences, reconstructing the fossilization environment and constraining preservation biases. Similarly, this approach could potentially provide new knowledge on other (bio)mineralization processes in environmental sciences. We also illustrate that mineralogical contrasts between fossil tissues and/or the encasing sedimentary matrix can be used to visualize hidden anatomies in fossils.

opencc-zeroAug 2020View details →

opencc-by-4.0Nov 2023View details →

Ocean Heat Content Anomalies in the North Atlantic based on mapping Argo data using local Gaussian processes defined over space

Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2005-2022, in the North Atlantic, north of 20N) subtracting the time mean over the period 2005-2021 from the monthly time series of OHC. OHC fields are mapped using a locally stationary Gaussian process (defined over space) with data-driven decorrelation scales (Kuusela and Stein, 2018).  A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In this product, mapping is done in latitude and longitude with monthly subsets of data. Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA for 0-2000 dbar. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.

opencc-by-4.0Mar 2024View details →

COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'

Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.

opencc-by-4.0Mar 2024View details →

Mapping Deformation Processes using InSAR PS+DS Timeseries Estimation in Northern California, U.S.

Open the record for dataset details and reuse information.

opencc-by-4.0May 2022View details →

DoTA-seq processed datasets (barcode: read mappings)

Processed DoTA-seq sequencing reads to generate barcode: read mapping tables. This data is further processed to generate figures 1-3.

opencc-by-nc-4.0Apr 2021View details →

Datasets and code for "Mapping the plague through natural language processing"

This project investigates the performance of various NLP libraries and geocoding services for the semi-automated generation of quantitative datasets from narrative texts. We provide the original files, several intermediate data products as well as the final plague datasets. Please note that some of the steps in this process were done manually, thus some of the scripts cannot be run completely.  This work is based on two plague treatises: - Sticker, G. 1908 Abhandlungen aus der Seuchengeschichte und Seuchenlehre. Band 1: Die Pest. Giessen, A. Töpelmann. - Biraben, J.-N. 1975 Les hommes et la peste en France et dans les pays européens et méditerranéens. Paris, Mouton. The final geocoded, plague datasets are: - plague_sticker_v1.csv - plague_biraben_v1.csv A data dictionary is available as plague_datadict.xlsx Other files: <table> <tbody> <tr> <td>file name</td> <td>content</td> </tr> <tr> <td>sticker_OCR_orig.txt</td> <td>Original OCR text</td> </tr> <tr> <td>sticker_OCR.txt</td> <td>Original OCR text without parenthesis (author names)</td> </tr> <tr> <td>sticker_textprep.rds</td> <td>Original OCR text with further preparations</td> </tr> <tr> <td>sticker_goldstandard_annotated_1.tsv</td> <td>manual annotations file 1</td> </tr> <tr> <td>sticker_goldstandard_annotated_2.tsv</td> <td>manual annotations file 2</td> </tr> <tr> <td>sticker_goldstandard_annotated_consensus.tsv</td> <td>consenus annotation file</td> </tr> <tr> <td>sticker_standard_toponyms.csv</td> <td>Gold standard for toponym recognition. Contains the tokenization, the start/end character respective to the OCR text (orig and without parenthesis) and whether a token is a location or other</td> </tr> <tr> <td>sticker_comparison_NER.rds</td> <td>Comparison of NER performance</td> </tr> <tr> <td>sticker_comparison_geocoding.rds</td> <td>Comparison of Geocoding performance</td> </tr> </tbody> </table>