dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, elevation data at 30m spatial resolution from NASA Shuttle Radar Topography Mission (SRTM) and annual mean climate variables (Terraclimate datasets at * 4km of spatial resolution) were used to analyse the forest and species distributions. We found that natural forests are currently more frequently found on south-facing slopes, likely because of geomorphology and past land use, and that Tibouchina is restricted to the wetter part of the region (southern part), which annually receives at least 1600 mm of precipitation. Tibouchina pulchra was found to clearly indicate forest regeneration as almost all individuals were found within or adjacent to forests regrown after 1962. By contrast, Cecropia hololeuca was found to indicate older disturbed forests, with all individuals almost exclusively found in forest fragments already present in 1962.
The classification accuracies above 95% for vegetation type and over 97% for C. hololeuca show that Unet outperforms other image segmentation methods and could support species mapping at regional scale  [14]. Since C. hololeuca is a pioneer species, the spatial distribution of the individuals was then used to estimate anthropogenic disturbances inside the natural forest fragments, which are not accessible by others means. Over this same region, we have now gathered several other available high or very-high resolution remote sensed datasets for use in association with deep learning results or models to support conservation efforts of the Atlantic forest, to evaluate humans impacts and to understand forest landscape history. In 1962, ten years before the launch of the first Landsat satellite, panchromatic aerial photographs were taken over São Paulo State by the company Aerofoto Natividade Ltda, including the region studied by  [14] , for the Brazilian Institute of Agronomy (Instituto Agronomico, IAC) at a spatial scale of approximately 1/25000.
This tree species has ideal characteristics to support remote sensing methods of disturbance estimation in the Atlantic forest; it is found mainly in disturbed and secondary forests, sometimes being the dominant species  [20] [21] [22] [23]. Its trees bloom synchronously in February, and these crowns covered with pink flowers can be visually detected in very high resolution satellite images. The combined map of Tibouchina pulchra and Cecropia hololeuca is useful to understand the forested landscape of this Atlantic forest region. Furthermore, high resolution climate and elevation data are available for this region. These datasets, along with tree species maps could bring new insights to species distribution models based on remote sensing data  [17]. For example, TerraClimate is a new dataset with a high spatial resolution (1/24˚, *4km) of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015  [24].
Forest cover in 1962 To assess the recent history of the current forest stands and to test how the pioneer tree species Tibouchina pulchra and Cecropia hololeuca were associated with the regeneration of abandoned pasturelands or disturbed forest, we used an independent dataset of aerial photographs taken in 1962 over the São Paulo State region. The aerial photographs were realised in 1962 by the company Aerofoto Natividade Ltda for the Brazilian Institue of Agronomy (Instituto Agronomico, IAC) at a scale of approximatelly 1/25000 and made available by the Brazilian Institute of Geography and Cartography (Instituto Geografico e Cartografico do Estado de São Paulo, IGC). The IGC data are not freely available but low resolution samples can be browsed at http://datageo. ambiente. sp. gov. br/app/# in the directory /Base Imagem/Imagens IGC/ IGC-Fotoíndice Vôo 1962.
The sample contained 40 aerial photographs covering the region of the "Rio do Chapeu" near São Luiz do Paraitinga (Fig 1b ), with an overlap of approximately 40% to 50% between each adjacent photography. The images were mounted in two panoramas using the software AutoStitch©  [28, 29]. Then, these two panoramas were registered using the 2017 WorldView-2 images in ArcGis 10. 4  [30]  with 290 and 343 control points, respectively, using a spline function for interpolation.
Climate data To test if the T. pulchra and C. hololeuca spatial distributions were related to local climate, we computed the mean annual values of 12 variables from the high-resolution global dataset Terraclimate at * 4 km spatial resolution (2. 5 arc-minute, 1/24th degree)  [24]. The climate variables used were actual evapotranspiration (mm), climatic water deficit (mm), Palmer drought severity index, reference evapotranspiration (mm), precipitation (mm), soil moisture (mm), downward shortwave radiation (Wm 2 ), maximum temperature (˚C), minimum temperature (˚C), vapor pressure (KPa), vapor pressure deficit (kPa) and windspeed (m. s -1 )  [24].
Elevation data To test if the T. pulchra and C. hololeuca spatial distributions were related to elevation (or elevation-related variables), elevation data from the shuttle radar topography mission at 30 m spatial resolution were used  [25]. From this dataset, we used the variable 'elevation' and computed five additional elevation related indices that consider the eight neighbor pixels: slope (˚); aspect, which indicates the compass direction that the slope faces (in ˚, where 0 is the North); topographic position index, which compares the elevation of the pixel to the mean elevation of the neighboring pixels (positive value indicates ridge, negative value indicates valley and near zero value indicates flat areas or areas of constant slope); terrain ruggedness index, an index of topographic heterogeneity computed as the sum of the change in elevation between the pixels and its eight neighbors cells (zero indicates no ruggedness); and, finally, roughness, computed as the difference between the maximum and the minimum value of a cell and its eight surrounding cells  [31, 32].
and Data augmentation was applied randomly to the input images, including 0/90/180/270r otations and changes in the brightness, saturation and hue by converting RGB to Brightness-Saturation-Hue space (BSH) and modulating the current values between 95%-110% for brightness, 95%-105% for saturation and 99%-101% for hue (as changes in the plant hues are not expected). Segmentation accuracy assessment. Three performance metrics were computed. First, the overall accuracy was computed as the percentage of correctly classified pixels. Second, the intersection over union (IoU) of the object class, which is the number of pixels labelled as object in both the prediction and the reference, divided by the number of pixels labelled as object in the prediction and in the reference. Third, the F1 score was computed for each class i as the harmonic average of the precision and recall, Eq 1, where precision was the ratio of the number of segments classified correctly as i and the number of all segments (true and false positive) and recall was the ratio of the number of segments classified correctly as i and the total number of segments belonging to class i (true positive and false negative).
To belong to a given class, the pixel prediction value must be greater than or equal to 0. 5. From the raster image of the predictions, we generated the spatial polygons of each classes. Algorithm. The model was coded in R language  [37]  with Rstudio interface to Keras  [35, 36]  and Tensorflow backend  [38]. R code is available upon request. The training of the models took * 2 to 20 hours using GPU on a Nvidia Quadro K6000 with a 12 GB dedicated memory. Prediction using GPU of a single tile of 16384 × 16384 pixels (* 67 km 2 ) took approximately 35 minutes. The code of the U-net model was adapted from the original U-net code developed for Keras and Rstudio, and is available here http://doi. org/10. 5281/zenodo. 3601503.
The research leading to these results received funding from the project  BIO-RED 'Biomes of Brazil -Resilience, Recovery, and Diversity ', which is supported by the  São Paulo Research Foundation  ( FAPESP ,  2015/50484-0 ) and the  U. K. Natural Environment Research Council  ( NERC ,  NE/N012542/1 ). F. H. W. has been funded by  FAPESP  (grant  2016/17652-9 ). A. S. acknowledges the support of the  FAPESP  (grant  2016/03397-7 ). M. P. M. A. has been funded by  ECOFOR Project, BIOTA-FAPESP  (grant number  2012/51872-5 ). A. L. C. R. has been funded by supported by the  State of São Paulo Research Foundation/FAPESP  as part of the project ECOFOR (Process number  2012/51872-5 ) within the  BIOTA/ FAPESP Program  -The  Biodiversity Virtual Institute  (www. biota. org. br) and co-supported by the  British Natural Environment Research Council/NERC  ( NE/ K016431/1 ).
M. G. F acknowledges the support of Capes through a postdoctoral fellowship. Y. T. has been funded by the project  EPITOME ANR-17-CE23-0009  of the  French National Research Agency (ANR). L. E. O. C. A. thank the support of  FAPESP  (grant  2013/50533-5 ) and  CNPq  (grant  305054/2016 -3 ). We also thank the  Amazon Fund  through the financial collaboration of the  Brazilian Development Bank (BNDES)  and the  Foundation for Science, Technology and Space Applications (FUNCATE)  no. 17. 2. 0536. 1  ( Environmental Monitoring of Brazilian Biomes ). The funders ( FAPESP ,  NERC ,  Capes ,  ANR  and  Luxcarta Technology ) provided support in the form of salaries for authors F. W. , A. S. , Y. T. and M. G. F. , but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The Worldview images and the 1962 aerial images are owned by be shared publicly. However, a code example of the U-net model with a simulated Worldview image for reproducibility is available (https://doi. org/10. 5281/ zenodo. 3601503) and the data of the maps presented in the Fig 8 are available on a public repository (https://doi. org/10. 5281/zenodo. 3601487). The authors confirm they had.
Data warehouse and Data Lake Data warehouses have been designed as an optimization of relational databases for querying, and are used to support decision making in organization. Conceptual models of data warehouses are based on the following concepts: facts and measures, dimension, hierarchies and members  [22]. In fact, designing a data warehouse is defining a space of possible cross-tabulations, that will be used by users to explore the data. Data warehouses enable the easy exploration of a large dataset by users. But the implementation of a data warehouse implies the normalization (and the automation of this normalization via ETL (Extract Transform Load) processes) of each entering data, from various data sources. Despite some propositions, the integration of documents and satellite images into a data warehouse is not a simple task.
Spatial Data Lake Design: a proposal To the best of our knowledge, there is no studies dealing specifically with the design of a Spatial Data Lake. In the rest of this paper, we present a new methodology in order to provide to the users a guideline for conceiving and implementing such a framework. The design is performed to store the data produced and used by the french metropolis: Montpellier Métropole Méditerranée (3M). Our case study dictates some constraints: spatial description of datasets and spatial analyses are essential, particularly, we need to store satellite images, the proposed system should be inter-operable with other local, national and European systems, the users need to explore the data lake in order to find relevant data, and eventually discover new ones. The proposed system is composed of three main parts: the data section, the metadata section and the inter-metadata section.
This norm includes spatial representations and is the basis of several metadata profiles (INSPIRE, Dublin core) used by public institutions. In the following, we present our proposal of extension. See Figure  1  to have an overview of the proposed conceptual model. The white classes come from  [19] , and the yellow classes are our additions. In order to lighten the figures, we represent only the classes and not the details containing with attributes. In the Data section (see Figure  2 ), we define a data lake as a set of resources. A resource can be a service (see  ISO 19115)  or a data series. A data series is composed of one (or more) datasets, that share a feature. A dataset is a collection of identifiable data. Three types of particular dataset are defined: document, vector and raster.
The Metadata section describes metadata records (see Figure  3 ). Each resource is associated to a metadata record. One metadata record is composed by: an identification (mandatory) that enables the recognition of each resource by users, a spatial representation (optional), a reference system (optional) and a spatial and/or temporal extent (optional) that describes the spatiality of the resource, a content description (optional), a lineage (optional), that explains how the resource has been obtained, one or several associated resources, a reference system information (optional), that identifying the spatial, the temporal, and parametric reference system(s) used by a resource, an extent, that describes the spatial and temporal extent of a resource. Finally, the Inter-Metadata section (see Figure  4 ) records relationships between datasets, and enables users to have a view of data related to their initial query.
Presentation of the infrastructure As described in the previous section, the data lake is composed of two parts: Data and Metadata. The Data section is based on a HDFS cluster. Our implementation uses three  Concerning the Metadata section, HDFS does not provide any indexing system nor a search engine. They have to be build upon the data lake by the administrator  [20]. Elasticsearch, based on Apache Lucene, meets those two needs  [8]  and  [20]. Our Metadata System implementation uses GeoNetwork  6. This web application embeds Apache Lucene and implements the ISO 19115 conceptual model. GeoNetwork stores the mandatory and optional metadata described in the previous section, including the HDFS path inside the Data Lake. So when the user queries the search engine, Discover and access to datasets using the datalake Users can discover and access the datasets using the GeoNetwork search engine.
Queries can be a combination of three dimensions: 1. semantic: based on keywords or on a full text search on title, abstract and lineage, 2. spatialized: drawing a bounding box on a map to filter by a geographical extent, 3. temporal: filtering by years, month and day. GeoNetwork returns a collection of metadata that describe the data. Users can browse these metadata and find HDFS links to download the corresponding dataset (Figure  6 ).
Deploy and populate the Data Lake We have automated the deployment and the data ingestion of the data lake through two steps. First, the system infrastructure must be created, configured and initiated. The data and the metadata zones are atomically deployed on four computers. Secondly, a python script must be started. Reading a CSV file, it will find links and data sources available on the Internet. Thanks to these information, the script will download data, insert them inside the data lake and reference them through GeoNetwork server.
Deploy a HDFS cluster and a GeoNetwork server The full cluster is deployed and maintained using these opensource projects: -Debian: Operating System for the virtual machines. The version 9 is used, instead of the last one (version 10), because of compatibility reasons. Indeed, Hadoop needs java version 9, which is no longer available in the latest version of debian, -VirtualBox as a hypervisor, -Vagrant as a configuration manager of virtual machines, Fig. 6. Discover and query datasets -Ansible as a software provisioning, configuration manager and application-deployment tool. The HDFS cluster can be deployed and configured using a single vagrant command line. Then the administrator has to connect to the namenode in order to format the file system. Afterwards, GeoNetwork can be deployed using a similar vagrant command line.
After starting the two parts (the metadata Management system and the HDFS cluster) of the data lake, four virtual machines are started and set-up, three for the hdfs cluster and one for the metadata management system. More technical information or instructions on how to deploy a HDFS Cluster with a GeoNetwork as a metadata system management, could be found in the README. md inside the git repository of our project. If default variables are used, the data lake file system can be browsed graphically by connecting to the web server of the namenode at: http://10. 0. 0. 10:9870. Other information, such as cluster health and log accessibility are also available. Populate the Data Lake The populating step is implemented by using two scripts written in python and R. Indeed, Python offers an excellent library to interact with HDFS, while R has interesting modules to manage ISO 19115 metadata.
In order to reduce the complexity generated by the concomitant use of these two languages, the R script has been encapsulated inside the Python script. Thus, the administrator only needs to run the Python script. As mentioned above, all the code files are available (see section 4. 4). Environment requirements (as dependencies) can be found in requirement. txt file inside the repository. Instructions on how to install and start the script are described in the README. md file. The main Python script works on five steps. First, it parses information given by 'datasources. csv' such as data provider, dataset name and keywords. Secondly, script browses data provider's website in order to build a json file that contains web-links to download the corresponding data. Then, these data files are downloaded and stored inside the data lake.
Finally, the R script is executed in order to create ISO 19139 (which is a standard of the ISO 19115 implementation into xml) xml files which are uploaded to the GeoNetwork. In Figure  7 , a screenshot of a metadata sheet of a dataset is presented. A set of data files is associated with a name and a link to download the data from the HDFS cluster. Data can be easily obtained by following the given link. The namenode of the HDFS cluster offers a REST API that ensures the transfer of the data to the user.
Example of an user query The user can build complex queries mixing the three dimensions: spatial, temporal and semantic. These queries are made through the GeoNetwork search engine. The three dimensions can be requested in a full text query, such as the following example: "3M mobility 2013". The search engine will propose all datasets which the temporal period includes or intersects the year 2013 and which the spatial extent includes or intersect 3M spatial extent. Finally, only the datasets that have the keyword "mobility" in their metadata, will be shown to the user. The spatial dimension can also be built by using a map. The user can draw a bounding box around a region. She/He gets a first filtering on the selected extent. She/He can enrich his query by adding in full text temporal and semantic constraints.
Data and Software Computational environment The computational environment (HDFS clusters and GeoNetWork) is provided by using four virtual machines. The implementation and ⧫ □ ✓ ✓ ✓ ✓ Alrehamy and Walker  [42]  ⧫ ✓ ✓ Data wrangling  [41]  ⧫ ✓ ✓ ✓ ✓ Constance [16] ⧫ ✓ ✓ GEMMS [32] ◊ ✓ CLAMS [12] ⧫ ✓ Suriarachchi and Plale [40] ⧫ ✓ ✓ Singh, K. et al. [39] ⧫ ✓ ✓ ✓ ✓ Farrugia et al. [13] ⧫ ✓ GOODS [17] ⧫ ✓ ✓ ✓ ✓ CoreDB [3] ⧫ ✓ ✓ Ground [18] ◊ □ ✓ ✓ ✓ ✓ KAYAK [26] ⧫ ✓ ✓ ✓ CoreKG [4] ⧫ ✓ ✓ ✓ ✓ ✓ Diamantini et al. [10] ◊ ✓ ✓ ✓ Ravat, F. , Zhao, Y. [34] ⧫ ◊ ✓ ✓ ✓ ✓ ✓ MEDAL [36] ◊ ✓ ✓ ✓ ✓ ✓ ✓ Our proposal ⧫ ◊ ✓ ✓ ✓ ✓ ✓ ✓ ⧫ : Data lake implementation ◊ : Metadata model □ : Model or implementation assimilable to a data lake ✓ : feature is available Table  1  shows the state-of-the-art approaches and the associated features provided.
It clearly shows how the two concepts are integrated to each other with a fully open source code. It also covers all the features described in Table  1 , while implementing the data lake and the metadata management system. Moreover, our approach solves problems related to complex data storage (e. g. spatial), and takes into account any type of data, thanks to the ISO 19115 standard. Also, it is easily reproducible and compatible with international catalogs systems. In terms of access, our system offers through GeoNetwork, an user interface that allows any user to explore or retrieve data from the lake. Our solution can be used by two types of users. Firstly, the 'general public' users or any user, the system allows them to explore and retrieve data available in the lake through the web interface offered by GeoNetwork without the need of data lake exploitation skills.
Acknowledgments This work was partially supported by  Montpellier Méditerranée Métropole  and by  Songe Project (FEDER and Occitanie Region). The experiments were also supported by public funds received in the framework of  GEOSUD, a project  ( ANR-10-EQPX-20 ) of the program " Investissements d'Avenir " managed by the  French National Research Agency.
The C-STAD model is a method that has been recently proposed to model and forecast cohort mortality  (Basellini et al. 2020). Specifically, the C-STAD is a relational model based on a warping transformation of the age axis of a reference distribution of deaths. The parameters of the transformation function capture mortality changes in terms of shifting and compression dynamics. Mortality forecasts are obtained from their extrapolation using standard time series models. The C-STAD is a generalization of the approach proposed by  Basellini and Camarda (2019)  to model and forecast adult period mortality from age-at-death distributions. Another method recently proposed to forecast cohort age-at-death distributions is based on the PCLM (Penalized Composite Link Model) for ungrouping data  (Eilers 2007; Rizzi et al.
Data Observed death rates for Denmark were extracted from the Human Mortality Database (HMD 2019) by sex, and life tables were constructed using the standard procedure  (Preston et al. 2001). When a death rate is equal to zero, the value was replaced by half of the minimum death rate observed in the dataset, as many of the models cannot be estimated with the presence of zeros. Zeros are, however, rare in the dataset. Overall, data from 1925 to 2016 for both females and males were extracted, but different fitting periods are used across the analyses. For the LL, CoDA-C and DG models, a reference population is needed. For the LL and CoDA-C models, the reference population is the average mortality trend for Denmark, Sweden, the Netherlands and the United Kingdom. The average is the geometric mean of the death rates of these four countries for the LL model and the associated life table distribution of deaths for the CoDA-C.
Data for these countries were also extracted from the HMD. The choice of the reference population is based on the analysis of  Kjaergaard et al. (2016). The selected reference population provides the most accurate forecasts for Denmark and consists of countries with similar mortality trends that are geographically close to Denmark. For the DG model, the reference population is the best practice in life expectancy, as defined by  Oeppen and Vaupel (2002)  and based on countries within the HMD that have the highest life expectancy each year  (Pascariu et al. 2018).
These models are based on cohort data only. In order to fit the models and complete the mortality experience of a cohort, partial information on this specific cohort is needed. Reliable estimates are obtained for cohorts born up to 1970 and 1960, for C-STAD and PCLM, respectively. Thus, the C-STAD and PCLM models cannot be used to forecast mortality of more recent cohorts. Table  7. 4 Cohort life expectancy at birth for specific cohorts forecast with eight models, the range of the forecast values across the eight models and range across the six models based on period forecasts.