dataesr/scientific-paragraphs-categorization huggingface dataset:
| Additional features or GTS2020 (the default). This functionality therefore enables numerical ages to be assigned to datasets only containing characterbased interval names (e. g. "Maastrichtian"). reefs <-look_up(occdf = reefs, early_interval = "interval", late_interval = "interval", int_key = interval_key) Finally, a common feature request from our survey (Figure  1 ) was the ability to add the 'Geological Time Scale' to time-series plots in base R, with similar behaviour to the deeptime R package  (Gearty, 2023)  for ggplot2  (Wickham, 2016). To fulfil this request, the axis_geo function has been developed for the palaeoverse package (Figure  5 ).
| RE SOURCE S To support the aims and use of palaeoverse, we have made several resources available to the palaeobiological community. First, we have built a package website (http://palae overse. palae overse. org), which provides information on how to contribute to palaeoverse, how to report issues and bugs, and a general community code of conduct. Second, we have established a Google Group to foster collaboration and discussion around the issues faced by the community, such as establishing standards for data preparation (https://groups. google. com/g/palae overse).
Funding information  ETH+ grant  (BECCY);  Fundação de Amparo à Pesquisa do Estado de São Paulo , Grant/Award Number:  2022/05697-9 ;  H2020   European Research Council , Grant/Award Number:  947921 ;  Juan de la Cierva -formación 2020 fellowship (European Union " NextGenerationEU"/PRTR) , Grant/ Award Number:  FJC2020-044836-I/ MCIN/AEI/10. 13039/501100011033 ;  Juan de la Cierva -formación 2021 fellowship ( European Union  " NextGenerationEU"/PRTR) , Grant/Award Number:  FJC2021-046695-I/MCIN/ AEI/10. 13039/501100011033 ;  Lerner-Gray Postdoctoral Research Fellowship  from the  Richard Gilder Graduate School (American Museum of Natural History) ;  Population Biology Program   of Excellence Postdoctoral Fellowship  ( University of Nebraska-Lincoln School of Biological Sciences) ;  Royal Society , Grant/ Award Number:  RF_ERE_210013 ,  RGF_ EA_180318  and  RGF_R1_180020.
DATA AVA I L A B I L I T Y S TAT E M E N T The palaeoverse R package is hosted on CRAN (https://cran. r-proje ct. org/web/packa ges/palae overs e/) and is available on GitHub (https://github. com/palae overs e-commu nity/palae overse). The code is also archived in Zenodo through continuous integration  (Jones et al. , 2023). All example datasets are bundled with the R package. All code is released under a GPL (>=3) licence.
Two occurrence datasets (tetrapods and reefs) are provided in palaeoverse to support reproducible examples within the function documentation. The tetrapods dataset is a compilation of Carboniferous-Early Triassic tetrapod occurrences (n = 5270) from the Paleobiology Database. The dataset includes variables relevant to common palaeobiological analyses, covering the taxonomic identification of fossils and their geological, geographical, and environmental context. The reefs dataset is a compilation of Phanerozoic reef occurrences (n = 4363) from the PaleoReefs Database (Kiessling.
, 2023), geosphere (Hijmans, 2022) , httr (Wickham, 2022) , h3jsr (O'Brien, 2023), pbapply (Solymos & Zawadzki, 2023) , sf (Pebesma, 2018) , stats* (R Core Team, 2022), utils* (R Core Team, 2022)  phylo_check Check taxon names against tips in a phylogeny and/or remove tips from the tree ape (Paradis & Schliep, 2019)  tax_check Check for spelling mistakes in taxon names and flag potential issues stats* (R Core Team, 2022), stringdist (van der Loo, 2014)tax_range_space Calculate the geographic range of taxa (choice of approaches) geosphere (Hijmans, 2022) , grDevices (R Core Team, 2022), h3jsr (O'Brien, 2023) tax_range_time Calculate and plot the temporal range of taxa graphics* (R Core Team, 2022) tax_expand_lat Convert taxon latitudinal ranges to bin-level pseudo-occurrences -tax_expand_time Convert taxon temporal ranges to interval-level pseudo-occurrences -tax_unique Calculate the number of unique taxa in a dataset of occurrences stats* (R Core Team, 2022) time_bins Generate stratigraphic time bins or near-equal length time bins -temporal bins at different temporal ranks (i.
e. stage, epoch, period, era, or eon) using these datasets for a specified interval input: # Get stage-level time bins time_bins(interval = "Phanerozoic", rank = "stage", plot = TRUE)As is evident from Figure2, GTS temporal bins are highly uneven in duration. Previous studies have attempted to circumvent this issue by generating near-equal-length time bins by grouping stages towards a target bin length (e. g. Close, Benson, Alroy, et al. , 2020; Mannion et al. , 2015). time_bins enables users to generate near-equal-length time bins following this approach (Figure3) to a specified target size:# Generate near-equal length time bins time_bins(interval = "Phanerozoic", rank = "stage", size = 15, plot = TRUE)Nevertheless, the appropriate set of time bins to use will depend upon the nature of subsequent analyses.
bins <-lat_bins(size = 15) # Assign occurrences to bins bin_lat(occdf = tetrapods, bins = bins) 3. 3. 4 | Palaeogeographical reconstruction Using the present-day coordinates of fossil occurrences, plate rotation models can be used to reconstruct their location at the time of deposition. Existing fossil databases provide reconstructed coordinates for occurrences from only one or two of the many plate rotation models available (if any), and it is not always clear which model (or version of the model) has been used. This lack of transparency is reflected in some published articles that only cite the use of GPlates to reconstruct palaeocoordinates, yet lack specifics on which plate rotation model was used with the GPlates Web Service or desktop application (Müller et al. , 2018). Furthermore, the uncertainty in palaeogeographical reconstructions is often underappreciated; reconstructed coordinates are treated as being well-established, rather than model-based estimates.
Finally, online databases do not provide palaeocoordinates for all known samples. Both published and unpublished data (e. g. museum specimens) exist outside of online databases for which researchers might require palaeocoordinates. We have developed the function palaeorotate to address this challenge. The function allows palaeocoordinates to be reconstructed within R using two different approaches: 'point' and 'grid'. The first approach uses the GPlates Web Service and allows point data to be rotated to specific ages using the available models (see https://gwsdoc. gplat es. org). The second approach uses reconstruction files of pre-generated palaeocoordinates to spatiotemporally link occurrences' modern coordinates and age estimates with their respective palaeocoordinates. These reconstruction files were generated using an equal-area hexagonal grid (~100 km spacings) via the h3jsr package(O'Brien, 2023)  and allow palaeocoordinates to be generated efficiently for large datasets.
# Palaeorotate reef dataset reefs <-palaeorotate(occdf = reefs, age = "interval_mid_ma") # Plot palaeolatitudinal distribution through time plot(x = reefs$interval_mid_ma, y = reefs$p_lat, xlab = "Time (Ma)", ylab = "Palaeolatitude (\u00B0)", xlim = c(541, 0), xaxt = "n", type = "p", pch = 20) # Add Geological Time Scale axis_geo(side = 1, intervals = "periods").
F Temporal range of tetrapod orders in the palaeoverse example dataset. additional researchers (survey participants). Our hope is that palaeoverse will evolve into a community-driven package by welcoming contributions from the wider palaeontological community to broaden the available functionality. To support this aim, we provide guidance on how community members can contribute to palaeoverse on the package website (http://palae overse. palae overse. org). Our working group also has the wider aim of establishing community standards and consensus in computational palaeobiological research and facilitating comparison across studies. Through the palaeoverse R package, we hope to assist in making code more familiar and readable to fellow researchers, prevent researchers from 'reinventing the wheel' for common procedures, and improve the overall reproducibility of research through the use of computational tools that have been vetted and accepted by the broader community.
Datasets are frequently explored within groups in palaeobiology, such as time bins, collections, or regions. The group_apply function 2041210x, 2023, 9, Downloaded from https://besjournals. onlinelibrary. wiley. com/doi/10. 1111/2041-210X. 14099 by Eth Zã¼Rich, Wiley Online Library on [02/10/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
Metodología Enfoque cualitativo mediante entrevistas semidirigidas. Etapas de recogida de datos: 1. Utilizando el QS World University Rankings 2022 como guía, las mejores universidades del país fueron identificadas. 2. Tras este proceso de identificación, se contactó y entrevistó a 8 científicos de estas universidades si es que estaban de acuerdo. 3. Las transcripciones de las entrevistas se analizaron con el software Nvivo 12. 4. El proceso de codificación se realizó mediante un proceso inductivo de desarrollo de categorías. Idea bastante imprecisa de si existe una política de ciencia abierta en su institución; o de lo que contiene. Por el momento, no hay cambios en cuanto a: Formato preferido para la difusión de resultados. Rutina o proceso de trabajo.
Using low-cost technologies to monitor microclimate is nothing new, and several solutions in the recent years based either on microcontrollers  (Mickley et al. , 2019)  or single card computers have been developed for that specific objective  (Sethi et al. , 2018)  or applied to broader applications in ecology (see the review of Raspberry Pi applications for biologists by  Jolles, 2021). However, to our knowledge, no development covered the whole cycle of data life, from data acquisition to data visualization, and taking into account the local constraints and diverse situations to power the devices and transfer the information to the server. While we focus on key features, extended materials and methods can be found in the Supporting Information section which contain all the information to reproduce the IoT microclimatic monitoring network, together with links pointing to upto-date software and codes.
| Overview of the IoT network The Internet of Things (IoT) is a network allowing objects to connect to the Internet and exchange information through standard protocols  (Hittinger & Jaramillo, 2019). The advantage of IoT over other information exchange technologies such as the Global System for Mobile communications network (GSM) is to be able to access information through Internet at a lower cost. The obstacles to adoption remain the technical complexity of deployment and the energy dependence. Here, we propose to meet the challenges related to technical complexity while optimizing energy dependence. Data are centralized on a server with a PostgreSQL database (any objectrelational database system can be used as an alternative). The interaction with the data is made possible thanks to a Representational State Transfer Application Programming Interface (RESTful API) allowing easy access to data.
It is developed on a Linux server with NGINX Open Source web server (pronounced "engine X"; suitable for heavy traffic thanks to its asynchronous management as opposed to traditional web servers such as Apache) and the Node. js JavaScript runtime together with the Express framework that provide features for web applications  (Mardan, 2018). Thanks to the API, data are delivered in Javascript Object Notation (JSON) format from gateways through the Internet. Here, a gateway is defined as a device that connect two systems with different data formats. Sensors can be directly connected to the gateways. The alternative is to connect the sensors to microcontroller units (MCUs) equipped with a Long Range (LoRa) Radio Transceiver allowing communication with the gateways using radio frequencies (Figure  1a ).
LoRa is a radio telecommunications protocol allowing the communication of connected objects at low speed (as opposed to the high speed offered by GSM/cellular networks), and at long distance (as opposed to the short distances of Bluetooth, Wi-Fi, Zigbee, Z-Wave). With the IoT, the LoRa protocol makes it possible to take advantage of both technologies to locally have a free low-speed communication protocol and at the global level a high-speed communication protocol to the server via the Internet (Figure  2 ). This solution makes it possible not to have to depend on subscription plans of information transmission (apart from the Internet), as opposed to commercial networks such as Sigfox or LoRaWan. Here, we used RFM95W LoRa modules from HOPE Microelectronics CO. The network is designed so as to place Rapsberry Pi-based gateways near any building with access to power outlets and Internet connection, ESP32-based gateways in the surrounding fields within the Wi-Fi range and powered by batteries and solar panels, and Arduino-based MCUs (called PI2Puino in the rest of the text) in distant fields within the range of the radio frequencies and powered either by batteries or rechargeable batteries and solar panels (Figure  1b ).
| Data acquisition Environmental variables are acquired using different sensors depending on the network level. For the MCU communicating with the gateway through LoRa (PI2Puino, see Figure  1b ), we used a temperature sensor (DS18B20; accuracy ±0. 5°C) together with a temperature and relative humidity sensors (DHT22; accuracy ±0. 5°C and ±2%RH), both minimizing the energy cost. For the Raspberry Pi and ESP32 gateways that take advantage of a power outlet or a solar panel, we used a temperature, relative humidity and pressure sensor (Adafruit module BME280/680; accuracy ±3%). Gateways are also equipped with a light sensor (Adafruit module SI1145 for visible, Ultra Violet, and Infra Red light). Although these sensors were chosen for their accuracy, genericity and their community of users, any sensor can be added or used as a substitute for those presented here.
| Data transfer Data acquired from the PI2Puino are sent using the embedded Once received by the Raspberry Pi-based gateway equipped with the RFM95W LoRa module, the gateway checks for data integrity (numerical value) and forwards data to the server using its gateway key and a JSON-encoded message. Data acquired from the sensors connected to the gateway are forwarded directly to the server. The Raspberry Pi-based gateway code was written using Python 3. 9. In case of an unhandled exception, the python script is relaunched automatically thanks to a systemd unit. The ESP32based gateway is not equipped with a RF95W LoRa module but it works in a very similar way, forwarding data to the server using a gateway key. While the Raspberry Pi-based gateway is connected to an electric outlet, the EP32-based gateway is powered by a solar panel and a 3.
| Data storage and availability Data received by the server are stored in a PostgreSQL database using the Sequelize Object Relational Mapping (ORM) tool, which allows to abstract the database software. Data can be retrieved through the RESTful API. Any sensor can be accessed directly by its identification number, and the results are returned in JSON format. The complete documentation using Swagger is available at https:// pi2p. ird. fr/docum entat ion/api/. Here is an example from sensor number 57 to request the last two records using curl (https://curl. se/) from a UNIX terminal, and the server response: 1 curl https://pi2p. ird. fr/sensors/57/datas?page_size=2 2 3 {"metadata":{ 4 "link":{"first":"https://pi2p. ird. fr/sensors/57/datas?page=0&page_ size =2","next":"https://pi2p. ird. fr/sensors/57/datas?page=1&page_size =2","last":"https://pi2p.
| Data visualization Data visualization is provided with the production server so as to have a quick overview of the data. It is not the preferred way to access the data, which should be done through the API. It is however convenient to check trends in time series using a dynamic and rich visualization tool from a web browser. We used the vis. js community edition browser based visualization library (https://visjs. org/; visnetwork) and the Plotly JavaScript Open Source Graphing Library (https://plotly. com/javas cript/; Figure  3a, b ). Information is displayed dynamically in real time for each sensor, sorted by institution and gateway.
| Deployment and cost The IoT microclimatic monitoring network was deployed in France, Kenya and Bolivia in contrasting environmental conditions (temperate climate near Paris in France, tropical high Andean mountain near F I G U R E 2 Overview of the IoT system. The boxes with dotted edges represent in (1) the data acquisition, in (2) the data transfer, and in (3) the data storage and availability. Data visualization is represented on the left. Here, a client is the computer that sends requests to a server. El Alto in Bolivia, and tropical low mountain in Nairobi Kenya). We gathered data for more than 2 years (from January 2020 to November 2022) at a frequency of 1 min on a selection of data corresponding to three Raspberry Pi-based gateways. We then compared temperature data with the WorlClim database  (Fick & Hijmans, 2017) , therefore addressing the long term (monthly) differences between macro and microclimate.
For each day, we calculated the minimum, maximum and average temperature, then we represented the data in the form of a boxplot with the monthly distributions, as well as the data extracted from the WorldClim as a reference. Regarding the cost associated with the different devices, an estimate was made of 93€ for the Raspberry Pi gateway, 75€ for the ESP32 gateway, and 44€ for the PI2Puino MCU on the basis of unit purchases (Table  1 ).
| Deployment and acquired data Collected data for the three sites were aggregated to perform a comparison with the WorlClim database  (Fick & Hijmans, 2017;  Figure  4 ). Although these aspects may be the subject of future developments, it is important to note that the gateways have not been configured to send back information in the event of an Internet connectivity failure or a failure from the server side. The missing data in February 2021 on the Kenyan site confirm this problem of the Internet connectivity failure, and the August 2022 data exemplified a server side problem (Figure  4b ). To cope with these problems, collected data are stored locally on the gateways so that data can be retrieved manually. In contrast, the PI2Puino device can save the data and send it back to the gateway for a period of a few days (depending on the frequency of data acquisition), but it cannot store this data for long term.
In addition, the gateway based on an ESP32-type MCU and powered by a battery charged by a solar panel can continue to operate in the absence of sunlight as long as the battery holds. At an acquisition frequency of one data item every 30 min, an autonomy of approximately 4 days was experimentally measured in complete darkness. The data generally follows trends of average monthly temperature from the WorldClim database  (Fick & Hijmans, 2017 ), but we can highlight differences in temperatures due to the location of the gateways (microclimate), for example in Nairobi where average daily temperatures were higher than expected (Figure  4b ). data transferred successfully corresponding to 1,649,460 readings). While we focus here on temperature, all environmental variables can be retrieved from the server API using any programming language.
As for the objective of our study to evaluate the capacity of a low-cost environmental data acquisition system for academic use, our data confirms this possibility in agreement with the existing literature  (Jolles, 2021). While the comparison of macro-and microclimatic data is limited here to monthly WorldClim data, the system presented A palette from blue to green to yellow to red has been used to represent temperature in each category. The grey bars correspond to missing data or not yet acquired data to date for November 2022. Each bar corresponds to a month, with different colours depending on the number of days on the temperature category. For example, in A, January 2020, 10 days had a minimum temperature between -5 and 0°C, 11 days between 0 and 5°C, 9 days between 5 and 10°C, and 1 day with missing data.
DATA AVA I L A B I L I T Y S TAT E M E N T All the codes presented in this paper are available in the supporting information S1, and in the project GitHub repository (https:// github. com/frare b/pi2p/) and associated Zenodo repository  (Rebaudo et al. (2022) ; https://doi. org/10. 5281/zenodo. 7479887). Codes are distributed under GNU AFFERO GENERAL PUBLIC LICENCE Version 3 as mentioned in the LICENCE file of the GitHub repository. The production server is available at https://pi2p. ird. fr/ and the associated documentation at https://frareb. github. io/ pi2p_docu/. The API documentation using Swagger is available at https://pi2p. ird. fr/docum entat ion/api/ and in the GitHub repository. The Data presented in the result section can be extracted directly from the server or can be found in the project dataverse repository at https://datav erse.
Chile is one of the Latin American countries most affected by Climate Change. There is a high level of uncertainty regarding the variability of precipitation and its projections in many regions of this country. This poses challenges for climate characterization and for defining strategies to reduce its risks. The study area is the Puna of Atacama Desert, Andean highlands located to the eastern side of the extreme arid lands, a region that concentrates the main copper and lithium mining at word scale, and where meteorological observations are scarce, with missing data and unreliable projections. Considering this data limitations, a daily precipitation database of 35 weather stations was constructed in order to evaluate some extreme precipitation indices that allow establishing changes between 1981-2017, in addition to spatial interpolations based on topography.
This is pointed out by  Souvignet et al. (2012)  and  Sarricolea et al. (2017) , highlighting the importance of the study of local climates in this regard. But not only that, because they can lead to actions that seek precautionary principles with respect to water resources, in order to achieve more sustainable forms among the various actors and agents that pressure and coexist in these territories. Thus, the novelty of this work lies in generating a daily resolution pluviometric database, generating indices of extremes occurrences and mapping this information based on a simple and replicable geostatistical method. This information is essential for making strategic proposals for resource use, as well as proposing improvements to the system for monitoring environmental variables continuously and minimizing gaps, in addition to mapping using methods that give importance to local climatology.
Data sources and methods Rainfall data from 35 meteorological stations , belonging to both the National Water General Direction (DGA for its Spanish acronym) and Meteorological Direction of Chile (DMC for its Spanish acronym; http://www. meteochile. cl), are used for this study (Table  1 ). The daily data were obtained from the repository of the Center for Climate and Resilience Research (CR2) of the University of Chile. All the data were quality-controlled daily scaled, with the deletion of suspicious values, outliers and suspicious dry/humid days according to  Serrano-Notivoli et al. (2017). After this step, precipitation data were estimated for all the missing days at every station. Both processes were afforded with the Precipitation extremes in the Puna of Atacama Desert, Chile: How to manage current and future uncertainty? reddPrec R package  (Serrano-Notivoli et al.