dataesr/scientific-paragraphs-categorization huggingface dataset:
By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots. net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change.
, 2002)  and later adapted to Africa (AfriTRON,  Lewis et al. , 2009)  and Southeast Asia (T-FORCES,  Qie et al. , 2017)  our approach encourages international grassroots initiatives and links them with standardized field methods and data management. Now, with ForestPlots. net (López-Gonzalez et al. , 2011 , 2015)  we support multiple networks with cyber-infrastructure that enables tropical scientists to do together what was previously impossible alone. Providing tools to ensure tropical scientists can manage, share and analyse their data themselves, ForestPlots. net is a global platform where data originators are in control and free to collaborate, support, or lead as much as they like. However, while much has been accomplished the wider challenges still run deep. Our aim of supporting the best possible science within a model of equitable access to data and other resources remains as much an aspiration as a claim of achievements already made.
net welcomes all contributors with carefullymanaged plots. In Africa, our early networking focussed on assessing whether there were similar patterns of changes in carbon stocks as observed in South American forests and the causes of such changes. Efforts began in 2001 to recensus many of the earlier plots installed in post-independence Africa (UK funding to O. Phillips, Y. Malhi and S. Lewis), which were later formalised as the African Tropical Rainforest Observation Network (AfriTRON;  Lewis et al. , 2009)  and catalysed a tripling of the African multi-census plot dataset over the last decade  (Hubau et al. , 2020). These span 12 African countries with moist forests from Sierra Leone in the west to Tanzania in the east. Like RAINFOR in Amazonia, AfriTRON campaigns to develop long-term plot networking in Borneo  (Qie et al.
, 2017) , and supported African recensuses  (Hubau et al. , 2020). While smaller than its Amazonian and African counterparts, the Asian network builds on plots installed by a number of foresters and botanists as long as 60 years ago. Critically, RAINFOR, AfriTRON, T-FORCES and TmFO use the same field and analytical protocols. How can we combine the different strengths of these and other initiatives to maximise their impact on science and society? To achieve this requires shared data management tools and horizontal organisational structures that foster leadership by tropical scientists. Our plot data management scheme was originally conceived in 2000 as a desktop database to support RAINFOR analyses of spatial variation in wood density, biomass, productivity, and changes in biomass over time  (Baker et al.
, 2004a (Baker et al. , , 2004b;; Malhi et al. , 2004). This was expanded to draw together inventory data from >100 sites in Amazonia and then African forest plots to include some of the longest running monitoring sites worldwide  (Peacock et al. , 2007). Since 2009 we have developed a Structured Query Language web application with sophisticated programming, providing a one-stop platform to a growing global community of contributors and users  (López-Gonzalez et al. , 2011). Now, ForestPlots. net supplies ecological informatics to colleagues in scientist-led networks from 54 countries working across 44 tropical nations (Fig. 1 ). Key advances in this platform include the ability to manage complex time-series data, track species linked to high-quality botanical records, and analyse records with common BiomasaFP R-language protocols  (López-Gonzalez et al.
With ForestPlots. net data contributors retain control and are able to manage, share and analyse their records using a common toolset. If new projects requesting to use their data are proposed they can agree to collaborate, or not, as they wish. Contributors often propose their own multi-site projects. ForestPlots. net can provide DOIs to datasets, further ensuring that contributors are properly acknowledged. Developing this functionality has supported a surge in multi-site and multi-national analyses that are increasingly initiated by scientists from the tropics, gradually supplanting the traditional model where researchers from the Global North lead. In sum, ForestPlots. net enables the level of control and collaboration that individual researchers wish for while also promoting network and multi-network integration.
In turn, this is empowering data owners and networks and helping to transform the face of tropical ecological science. The networks and ForestPlots share a 20-year history, but as we have seen the history of plot monitoring is much longer. The first recorded census in ForestPlots. net dates from 1939 in Budongo, Uganda. Forty years later, 676 censuses had been completed from 90 plots, but since 1979 fieldwork has accelerated greatly with >10,000 censuses completed across 4000 plots by 2020 (Fig. 2a ). This acceleration is reflected by the growing community of contributors, which by 2020 had reached 2000 individuals (Fig. 2b ). ForestPlots. net itself has grown steadily both in terms of censuses uploaded and in outputs (Fig. 3 ). The neotropics dominate much of this inventory and monitoring effort as well as the growth of ForestPlots.
Challenges and the future of tropical forest monitoring Large-scale plot networks have not only made a series of crucial scientific discoveries and advances, but even more profoundly the Social Research Network model pioneered by RAINFOR since 2000 has influenced how the science itself is being done. Tropical ecology has undergone a remarkable shift from a small cadre of researchers working in one or two sites to a more globalized and decentralised process with greatly increased contributions from tropical scientists. This has been made possible by supporting highly-distributed researchers and field sites, establishing mechanisms for shared data management, fostering an equitable concept of data ownership, and embracing groups who are often marginalised in research. Importantly, the network model is nurtured by researchers placing trust in the sharing of hard-won data to answer big questions and recognising the value of developing trusting relationships over time.
D. and mentoring beyond the doctoral degree is also important. To help, ForestPlots. net has made shared tools widely available, and especially data management analytic tools that support data contributors as much as users. To ensure fieldwork is valued and leadership in tropical researchers is fostered, we have developed a Code of Conduct to encourage contributions, support scientists in tropical countries, and promote mentoring of junior scientists. To oversee this we created a diverse steering committee that now supports dozens of projects each year (http://www. forestplots. net/en/join-forestplots/research-projects). As a result, the proportion of ForestPlots. net research projects and products led by tropical nationals has greatly increased, with <10% of publications when RAINFOR began  (2000) (2001) (2002) (2003) (2004) , rising to 35% in 2009 and 50% by 2019.
How should we value and recognise collaboration and leadership? Most of the obvious reward structures in science -job security, income, grant success, peer reputation and public acclaimcan favour a 'me first' approach. Credit accrues to individuals, but true collaboration involves trust, sharing and encouraging others. Collaboration is gratifying, but letting go of our egos can be challenging, while in larger groups there is greater risk that individuals feel their contributions go unnoticed. Likewise, the essential and major effort needed 'backstage' in ForestPlots. net to check data, update and develop data management, and support requests to utilize data, goes unseen. A partial developmental solution to this involves providing network contributors the opportunity to lead analyses with the expectation that these new leaders then support others with their analyses.
Box 1 What does it take? Clearly, long-term, ground-based monitoring of tropical forests requires a sustained global team effort. But just how much does it take to deliver tropical forest plot data in practice? It requires both skilled labour and funds. So here we address this question in terms of the human effort made thus far and the financial investment needed to monitor across continents. (a) The Human Contribution: Network efforts include not only in-country field campaigns but much besides. To deliver from conception to product, high-quality data collected over many years and in dozens of countries requires multiple teams that are well-led and consistently trained in the proper protocols, quality control, and data management. In RAINFOR and AfriTRON this includes national or local field-team members to establish and remeasure plots, others to collect and identify plants and collect and analyse soils, colleagues to organize and manage the data, and others to sustain and lead the process nationally and globallynot to mention those who support these processes with essential administration, herbarium assistance, database development, analytical packages, information technology support, technical training and so on.
Scientists would ask and likely argue: Monitoring what? For whom? With what precision, level of confidence, or spatial and temporal resolution? Recognising such difficulties, we take a pragmatic approach and reframe the question. Instead we posit, How much will it cost to monitor tropical forests using all the permanent plots that have already been remeasured? This question is tractable practically (these plots represent a known quantity: we know exactly where they are, what most of the species are, and to a large extent who can actually do the workeach of which is critical), it makes sense scientifically (the plots already have a baseline monitoring period against which we can assess any change, which is essential), and it is justifiable quantitatively (using somewhat smaller datasets than this we have already detected long-term changes in carbon balance, productivity and tree mortality on each continent, reported short-term changes in response to El Niño droughts and other climate anomalies, and attributed changes in carbon and biodiversity to climate drivers, all of which establish proof-of-concept).
Achievements, impact and potential Despite the challenges, tropical forest science has come a very long way. Until recently, tropical ecology suffered from a massive data deficit. We had plenty of theory and conjecture, but few comparable observations over time and space to deductively put these ideas to the test or inductively generate new ones. Networks such as ForestGEO, RAINFOR, AfriTRON, and the wider ForestPlots community have contributed much to resolving this. By leveraging a remarkably old technology, forest plot networks have sparked a modern revolution in tropical forest science. They provide the means by which we have quantified the trajectory of tropical forest carbon balance, including its climate sensitivity, and now provide a Pan-Tropical Observatory for tracking these vital indicators of Earth's health going forward.
CRediT authorship contribution statement All authors have contributed to ForestPlots. net-associated networks by leading, collecting or supporting field data acquisition, or implementing and funding network development, data management, analyses and outputs. O. L. P. wrote the manuscript with initial contributions from S. L. L. , M. J. S. contributed new analyses, M. J. S. , G. L. P. and A. L. helped prepare the figures, and all authors reviewed the manuscript with many suggesting valuable edits. O. While $6. 6 million is a significant sum it is instructive to compare it to funding required for other large-scale science initiatives. The United States alone spends $80 million annually (i. e. , twelve times as much) on its national forest inventory  (Castillo and Alvarez, 2020). Space Agencies invest from ca.
Acknowledgments This paper is a product of the RAINFOR, AfriTRON and  T-FORCES  networks and the many other partner networks in ForestPlots. netwhich support long-term forest science and monitoring across tropical countries. These initiatives have been supported by numerous people and grants since their inception. We are particularly indebted to more than one thousand four hundred field assistants for their essential help in establishing and maintaining the plots, as well as highly distributed rural communities and institutions. For additional assistance we thank  Michel Baisie ,  Wemo Betian ,  Vincent Bezard ,  Mireille Breuer-Ndoundou Hockemba ,  Ezequiel Chavez ,  Douglas Daly ,  Armandu Daniels ,  Eduardo Hase ,  Muhammad Idhamsyah ,  Phillipe Jeanmart ,  Cisquet Keibou Opepa ,  Jeanette Kemp ,  Antonio Lima ,  Jon Lloyd ,  Mpanya Lukasu ,  Sam Moore ,  Klaus Scipal  and  Rodrigo Sierra.
We thank  Mark Burkitt  for help developing the ForestPlots. netdatabase. We acknowledge the long-term help provided by national and local government offices in all countries where colleagues work in facilitating the permission and documentation for fieldwork, as well as help provided by protected area and other authorities. The networks have been supported by multiple grants, most notably the  European Research Council  ( ERC Advanced Grant   291585 -'T-FORCES' ), the  Gordon and Betty Moore Foundation  (# 1656  'RAINFOR', and # 5349  ' MonANPeru '), the  David and Lucile Packard Foundation , the  European Union 's Fifth, Sixth, and  Seventh Framework Programme  ( EVK2-CT-1999-00023 -'CARBONSINK-LBA ', 283080 -' GEO-CARBON ', 282664 -' AMAZALERT '), the  Natural Environment Research Council  ( NE/D005590/1 -'TROBIT', NE/F005806/1 -'AMAZONICA', 'PPFOR' E/M0022021/1 ), several  NERC Urgency and New Investigators Grants , the  NERC/State of São Paulo Research Foundation (FAPESP)  consortium grants ' BIO-RED' (NE/N012542/1 ,  2012/51872-5 ), ' ECO-FOR'  ( NE/K016431/1 ,  2012/51509-8 ), 'ARBOLES' ( NE/S011811/1 ), 'SEOSAW' ( NE/P008755/1 ), ' SECO ' ( NE/T01279X/1 ), ' NORDESTE ' ( NE/N012550/1 ,  2015/50488-5 ), the  Royal Society  ( University Research Fellowships and Global Challenges Awards ) ('FORAMA',  ICA/ R1/180100 ), the  National Geographic Society , the  Centre for International Forestry (CIFOR) ,  Gabon's National Parks Agency (ANPN) ,  US National Science Foundation  ( DEB 1754647 ), and  Colombia's Colciencias.
We acknowledge grant CEBA (ref. ANR-10-LABX-25-01 ) and the support of the  Forestry Development Authority of Liberia. We also acknowledge the support of the  European Space Agency. Data from  RAINFOR, AfriTRON  and  T-FORCES  are stored and curated at ForestPlots. net, a cyber-infrastructure initiative developed at the  University of Leeds  that unites permanent plot records and supports scientists from the world's tropical forests. The development of ForestPlots. netand curation of data has been funded by several grants including  NE/B503384/1 ,  NE/N012542/1  ' BIO-RED ',  ERC Advanced  Grant  291585 'T-FORCES', NE/F005806/1  'AMAZONICA',  NERC  New Investigators Awards,  NE/N004655/1 , 'TREMOR', the  Gordon and Betty Moore Foundation  (' RAINFOR', 'MonANPeru '),  ERC Starter  Grant  758873  'TreeMort', EU Framework 6, a  Royal Society University Research Fellowship , and a  Leverhulme Trust Research Fellowship.
Biota ColomBiana Reptiles. Se conoce que la producción de señales acústicas en reptiles ha evolucionado en cocodrilos, tortugas, lagartos y en menor medida en serpientes  (Frankenberg & Werner, 1992; Chen & Wiens, 2020). También está establecido que la mayoría de reptiles tienen cierta capacidad de percibir sonidos, pero sólo en algunos grupos se ha comprobado comunicación acústica entre conespecíficos  (Dooling et al. , 2000 , Rohtla et al. , 2019). En lo referente a Colombia, el segundo país del planeta con mayor diversidad de reptiles (> 600 especies; The Reptile Database, http://www. reptile-database. org), los estudios acerca de la producción de señales acústicas en estos vertebrados son muy escasos. La producción de señales acústicas se ha registrado por lo menos para 26 especies de reptiles presentes en Colombia; 21 de ellas son nativas y cinco son introducidas; sin embargo, estudios hechos directamente en Colombia sobre esta temática se limitan a una especie de cocodrilo (Crocodylus intermedius) y cuatro especies de tortugas (Chelonoidis carbonarius, C.
Para cada taxón, a partir de los puntos de presencia, se calculó el porcentaje de inclusión en áreas protegidas. A partir del modelo digital de elevación se extrajeron los valores promedios, mínimos y máximos de altitud para cada taxón, usando ArcGis 10. 2 (Esri 2013). Se comprobó la normalidad de los datos de esta variable mediante la prueba de Shapiro-Wills y la homogeneidad de varianza a través de la prueba de Levene. Debido al incumplimiento de ambas premisas se utilizó una prueba de Kruskal-Wallis y la prueba de Dunn como análisis de comparación múltiple de media a posteriori, para comparar las diferencias de los promedios de altitud entre los taxones. Estos procedimientos fueron realizados en el entorno R (R Core Team 2020) mediante la plataforma web univariados ga llery github. com/VMRA/univariados_gallery), con un nivel de significación estadística de p = 0.
Trends in International Migrant Stock: Migrants by Age and Sex (United Nations database ,  POP/DB/MIG/Stock/Rev. 2010 ). Recuperado de internet: http://www. un. org/en/development/desa/population/migration/data/index. shtml. ONU. (2013). United Nations,  Department of Economic and Social Affairs, Population Division. Trends in International Migrant Stock: Migrants by Age and Sex (United Nations database ,.
Geografía El área de trabajo (Figura 1) que presentamos en esta contribución se denomina Vallecitos y se encuentra en el Cordón del Plata, Cordillera Frontal, Andes Centrales de Mendoza entre los 32°55' y 32°59' S y entre 69°19' y 69°26' O. La orientación de la cuenca con los humedales es NNE-SSO. Se encuentra localizada en el Parque Natural Cordón del Plata y forma parte de una red de monitoreo de capa activa y permafrost para la red del Sistema Mundial de Observación del Clima (SMOC) y el National Snow and Ice Data Center (NSIDC, Colorado; http://nsidc. org/data/ -World Meteorological Organization dentro del programa GLOBAL CRYOSPHERE WATCH (GCW) -CryoNet South America). Su límite norte está marcado por el río Mendoza, al oeste el límite está dado por un imponente cordón montañoso con picos de hasta 6.
Análisis multitemporal Respondiendo al objetivo del trabajo sobre cambios en la dinámica de variación en el crecimiento de los mallines de altura se recopilaron imágenes satelitales y una foto aérea del año 1963 para observar esos cambios a lo largo de un tiempo cercano. La digitalización de estos humedales se llevó a cabo utilizando imágenes satelitales obtenidas de Google Earth, imágenes LANDSAT 7 ETM+, provistas gratuitamente por la NASA (LANDSAT, 2017) y una fotografía aérea provista por Unidad de Geocriología perteneciente a un vuelo programado para estudios científicos. Se utilizaron los datos de estaciones meteorológicas presentes en la cuenca para aportar información de campo que contribuya comprender los cambios observados (ver Tabla 2). El proceso de análisis multitemporal se realizó mediante el software QGIS 3.
4. 9. En este software se georreferenciaron las imágenes de Google Earth, LANDSAT (2017) y la fotografía aérea utilizando puntos GPS obtenidos en el terreno. Luego se analizaron diversos índices y modelos de humedad de suelo, vegetación, temperatura del aire y suelo para ahondar en la dinámica de fenómenos naturales en la zona de estudio. A partir de un Modelo de Elevación (DEM) se calcularon la orientación del valle, su exposición, radiación solar y pendientes del área de estudio. Por último, se realizaron análisis geométricos (área, longitud, ancho, pendiente) de los mallines mapeados para conocer sus cambios en los diversos años del periodo considerado. A partir de la información obtenida se busca conocer los cambios evidenciados anualmente en las vegas en el periodo 2002-2019 y esbozar preliminarmente las causas de estos cambios (Figura 3).
ACKNOWLEDGEMENTS We would like to thank  Caribaea Initiative  and the  National Geographic Society  ( WW-054R-17 ) for providing the financial support for the fieldwork. Also, we would like to give special thanks to professors  Frank Cézilly  and  Pablo F. Weaver  for their advice. Thanks to  Allana Kasheena Cumberbatch  and to our Haitians colleagues for assistance during fieldwork, especially to  Pierre Michard Beaujour  and  Wilson Celestine , as well as Sr. Loius Michelet and  Sr. Jouthe Joseph  from the  Ministry of Environment of Haiti  for permits.
METODOLOGÍA Con el fin de comprobar la evolución y el desarrollo que las metodologías de investigación de Diseños de Evaluación Experimental, Cuasi Experimental y de Caso Único han tenido en el Trabajo Social en las últimas décadas, se lleva a cabo un estudio utilizando la base de datos " Scopus" (Expertly curated abstract & citation database). Se utiliza Scopus porque es una base de datos internacional y multidisciplinar de prestigio mundial, que permite analizar las mejores publicaciones indexadas y validadas por la comunidad científica. Scopus es la mayor base de datos de resúmenes y citas de literatura revisada por pares: revistas, libros, actas de congresos y publicaciones comerciales. El acceso a Scopus proporciona una visión completa de la producción mundial de investigación en los campos de la ciencia, la tecnología, la salud, las ciencias sociales y las humanidades.
AGRADECIMIENTOS Agradecemos especialmente a la Dra. Alejandra Auer que hizo sugerencias para mejorar la encuesta a través de la lectura crítica, a las 15 personas que participaron en la prueba piloto, a todas las personas que colaboraron en la difusión de la encuesta a través de diferentes medios y redes sociales, y a todas las personas que respondieron el instrumento haciendo posible este trabajo. -Guerrero, L. , Maas, G. , &Holand, W. (2015). Desafíos en la gestión de residuos sólidos para las ciudades de países en desarrollo. Tecnología en  Marcha, 28,.
Antes de usar estos valores, es mejor validar los propios. A continuación, se muestran algunas herramientas útiles: ▶ El directorio internacional de la tabla/base de datos de composición de alimentos (INFOOD) contiene todos los cuadros oficiales de composición nutricional de los productos alimenticios medidos, y se ha actualizado constantemente desde 1988. Hay enlaces a bases de datos regionales que contienen información sobre alimentos específicos para las regiones. http://www. fao. org/infoods/infoods/ tables-and-databases/en. ▶ Wolphram Alpha es un ejemplo de una herramienta gratuita en la que, al introducir su producto, se obtienen sus valores nutricionales, incluso si los valores proporcionados son para "productos estándar" y que puedan existir diferencias con estos valores según las características específicas del producto.