dataesr/scientific-paragraphs-categorization huggingface dataset:
Raster file with LUC maps seriesGlobal General Land Use Cover Datasets with a Time Series of Maps Code Label LC Map 2015 -Raster file with LUC map LC maps full 1992-2015 series 130 Grassland 140 Lichens and mosses 150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%) 152 Sparse shrub (<15%) 153 Sparse herbaceous cover (<15%) 160 Tree cover, flooded, fresh or brackish water 170 Tree cover, flooded, saline water 180 Shrub or herbaceous cover, flooded, fresh/saline/brackish water 190 Urban areas 200 Bare areas 201 Consolidated bare areas 202 Unconsolidated bare areas 210 Water bodies -Code Label No data Cropland, rainfed Herbaceous cover Tree or shrub cover Cropland, irrigated or post-flooding Mosaic cropland (>50%)/natural vegetation (tree, shrub, herbaceous cover) (<50%) Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%) Tree cover, broadleaved, evergreen, closed to open (>15%) Tree cover, broadleaved, deciduous, closed to open (>15%) Tree cover, broadleaved, deciduous, closed (>40%) Tree cover, broadleaved, deciduous, open (15-40%) Tree cover, needleleaved, evergreen, closed (>40%) Tree cover, needleleaved, evergreen, open (15-40%) Tree cover, needleleaved, deciduous, closed to open (>15%) Tree cover, needleleaved, deciduous, closed (>40% Tree cover, needleleaved, deciduous, open (15-40%) Tree cover, mixed leaf type (broadleaved and needleleaved) Mosaic tree and shrub (>50%)/herbaceous cover (<50%) Mosaic herbaceous cover (>50%)/tree and shrub (<50%) Shrubland Evergreen shrubland Deciduous shrubland (continued) Tree cover, needleleaved, evergreen, closed to open (>15%) 220 Permanent snow and ice.
Raster file with multiple bands, including LUC information in five different classification schemes and data quality (. hdf) -PDFs with technical information about the productLegend and codificationMCD12Q1 is distributed for six different, widely used classification schemes. The only one displayed here is the IGBP scheme, which is one of the most commonly used. However, more information about the codes and class descriptions for the other classification legends is available in the user guide for this dataset(Sulla-Menashe et al. 2019). MCD12Q1 (500 m) -MCD12Q1-IGBP (International Geosphere-Biosphere Programme) 1 Evergreen 10 Grasslands needleleaf forests 2 Evergreen broadleaf 11 Permanent wetlands 1 forests Broadleaf evergreen 11 Cropland 3 Deciduous forest 12 Croplands 2 needleleaf forests Broadleaf deciduous 12 Paddy field 4 Deciduous forest 13 Urban and built-up lands 3 broadleaf forests Needleleaf evergreen 13 Cropland/other 5 Mixed forests forest 14 Cropland/natural vegetation mosaic 4 Needleleaf deciduous 14 vegetation mosaics Mangrove 6 Closed shrublands forest 15 Permanent snow and ice 7 5 Open shrublands Mixed forest 16 15 Barren Wetland 8 6 Woody savannas Tree open 17 16 Water bodies Bare area, consolidated 9 Savannas 18 Unclassified (gravel, rock) 7 Shrub 17 Bare area, unconsolidated (sand) Practical considerations 8 Herbaceous 18 Urban 9 Herbaceous with 19 Snow/ice Users can consult the dataset online through the Web Map sparse tree/shrub Service (WMS) available here.
Raster files with LUC maps for each decade -Text document with technical information and the legend Net land changes -Raster files with LUC maps for each decade -Text document with technical information and the legend Transitions maps (for gross and net) Project CORINE Land Cover (CLC) is a European project monitoring Land Use and Cover that dates back to 1985. It aims to map land uses and land covers across the whole continent according to the same rules. It is currently part of the land monitoring efforts of the Copernicus programme. HILDA gross and net transitions maps (2000-2010) Grassland to forest 141 Grassland to settlement 353 Other land to forest 151 Other land to 363 Water to forest 116 Water to settlement 335 Other land to forest settlement 121 Settlement to cropland 336 Water to forest 161 Water to settlement 445 Grassland to other 131 Settlement to forest 343 Forest to grassland land 141 Settlement to grassland 353 Forest to other land 223 Cropland to forest 446 Grassland to water 151 Settlement to other land 363 Forest to water 224 Cropland to grassland 454 Other land to grassland 161 Settlement to water 445 Other land to 225 Cropland to other land 464 Water to grassland grassland 226 Cropland to water 556 Other land to Water 223 Forest to cropland 446 Water to grassland 232 Forest to cropland 565 Water to other land 224 Grassland to cropland 454 Grassland to other land 225 Other land to cropland 464 Grassland to water Gross land changes Practical considerations 226 232 HILDA gross and net transitions maps (2000-2010) Water to cropland 556 Water to other land Cropland to forest 565 Other land to Water 112 Settlement to cropland 242 Grassland to cropland 113 Settlement to forest 252 Other land to cropland 114 Settlement to grassland 262 Water to cropland 115 Settlement to other land 334 Forest to grassland This is a valuable dataset because of the rich historic LUC information it provides.
There are very few long, dense historical series of LUC maps that measure LUC change over time. Nonetheless, users should be aware of the uncertainties associated with this dataset. The maps prior to 1950 were created by extrapolating the patterns of change for the period 1950-2010. This could introduce a high --Raster files with LUC maps for each decade -Text document with technical information and the legend degree of uncertainty. An online visualization of the maps for the years 1900 and 2010 is available, together with other auxiliary infor- 116 121 Settlement to water Cropland to settlement 335 336 Forest to other land Forest to water mation, at http://www. geo-informatie. nl/fuchs003/#. To study global historical LUC change at a similar level of detail, users should refer to the associated HILDA+ 131 Forest to settlement 343 Grassland to forest project.
M. Á. Castillo-Santiago (&) Á E. Mondragón-Vázquez Á R. Domínguez-Vera Departamento de Observación y Estudio de la Tierra, la Atmósfera y el Océano, El Colegio de la Frontera Sur, San Cristóbal de las Casas, Mexico e-mail: mcastill@ecosur. mx © The Author(s) 2022 D. García-Álvarez et al. (eds. ), Land Use Cover Datasets and Validation Tools, https://doi. org/10. 1007/978-3-030-90998-7_6.
M. Paegelow (&) Département de Géographie, Aménagement et Environnement, Université Toulouse Jean Jaurès, Toulouse, France e-mail: martin. paegelow@univ-tlse2. fr D. García-Álvarez Departamento de Geología, Geografía y Medio Ambiente, Universidad de Alcalá, Alcalá de Henares, Spain © The Author(s) 2022 D. García-Álvarez et al. (eds. ), Land Use Cover Datasets and Validation Tools, https://doi. org/10. 1007/978-3-030-90998-7_12.
Discussion and conclusions Complex networks constitute a straightforward and versatile representations of brain activity. However, comparing networks is in general an arduous tasks. Here we propose a method that greatly simplifies it, wherein networks are characterised in terms of the temporal rank stability of link and node-based features. We used ranked stability to compare resting brain activity of patients suffering from various diseases with healthy young and ageing subjects. Figure  6. Results for the upsampled data set. Evolution of the logarithm of the probability of finding the most frequent link with highest weight (top panels), and of the most frequent node with highest centrality (central panels) and highest clustering coefficient (bottom panels), as a function of the length of the window used to reconstruct the networks.
Materials and methods Main data set, control subjects and patients recruiting and selection. Ninety-eight participants were included in the study with six different groups, namely: healthy young, healthy elderly, MCI, AD, PD-MCI, and PD-D. Demographic information of the participants is presented in Table  3. The study included participants with the diagnosis of amnestic MCI (aMCI) according to the NIA-AA criteria  71  ; with PD-MCI according to the Movement Disorder Society (MDS) Level2 criteria  72  and age-, gender-and education-matched healthy elderly controls. Patients with aMCI and PD-MCI were recruited from an outpatient memory clinic and a movement disorders outpatient clinic of a university setting, respectively. Healthy controls were recruited from various community sources. The healthy elderly volunteers were included when no neurological abnormality or no global cognitive impairment (Mini-mental State Examination (MMSE) score ≥ 27 ) were determined.
Participants with depressive conditions, assessed as a Geriatric Depression Scale > 13 , were excluded from the study  79, 80. The study conformed to the principles of the Declaration of Helsinki. All participants and/or their relatives provided informed consent for the study, which was approved by the local ethical committee (Istanbul Medipol University Ethical Committee, Report No: 10840098-604. 01. 01-E. 8374). Main data set, electroencephalographic data recording. EEG was recorded in two different centres (The Istanbul Medipol University Hospital, REMER, Clinical Electrophysiology, Neuroimaging, and Neuromodulation Laboratory and the Izmir Dokuz Eylül University Multidisciplinary Brain Dynamics Research Center) with the same recording system and recording protocol. EEGs were recorded in a dimly lit, soundproof, electrically shielded room.
A BrainAmp 32-channel DC system (Brain Product GmbH, Germany) was used for recordings with a 500 Hz sampling rate and 0. 001-250 Hz band limits. Elastic caps (EasyCap GmbH, Germany) on which 32 Ag/AgCl electrodes were placed according to the 10/20 system were used for EEG recording. All electrode impedances were kept below approximately 10 k. As the online reference two additionally linked electrodes (A1+A2) were placed on the earlobes. Also, two electrodes were used as the electrooculogram which was placed on the medial upper and lateral orbital rim of the left eye with Ag/AgCl electrodes. Spontaneous EEG recordings were performed in sessions of 8 min (i. e. approximatively 240,000 data points per channel and subject), of which the first four corresponded to an "eyes open" condition, and the latter four to "eyes closed".
Validation data set, participant recruiting and electroencephalographic data recording. To support the validation of results obtained in the main data set previously described, a second EEG data set of PD patients was recorded at Istanbul Medipol University Hospital in Istanbul. PD patients were diagnosed according to the criteria of "United Kingdom Parkinson's Disease Society Brain Bank"  81. The Unified Parkinson's Disease Rating Scale (UPDRS) was used in order to determine the clinical features of PD; and the Hoehn-Yahr scale  82  was used to determine the disease stage. A total of 74 patients (ages 56-86, median of 74) and 22 matched control subjects (ages 54-89, median of 67) compose this validation data set. All patients with PD were evaluated 60-90 min after their morning dose of levodopa for the EEG recordings.
The EEG signals were recorded in a dimly isolated room with a Brain Amp 32-channel DC system machine (Brain Product GmbH, Germany) from 32 different electrodes which were arranged according to the international 10/20 system. The sampling rate was 500 Hz with band limits of 0. 01-250 Hz. All impedances were kept below 10 Kohm and two additional linked earlobe electrodes (A1+A2) served as reference electrodes. Electrooculogram was recorded with two electrodes placed in the medial upper and lateral orbital rim of the left eye. As in the case of the previous data set, spontaneous EEG recordings correspond to two 4-min sessions, respectively for "eyes open" and "eyes closed", with a 19-in. computer monitor performing as a black screen without fixation cross. No additional data preprocessing has been carried out and broadband signals have been used.
Reconstructing functional networks. For each subject, the original data comprised a set of N = 32 time series X, corresponding to the 32 EEG channels, each one of length l. In what follows, the notation X (t) c , with t ∈ [1, l] , will be used to indicate the t-th element of the time series c. These time series are split into non-over- lapping windows W of size w, yielding a total of ⌊l/w⌋ windows. In a way similar to the previous notation, W (i) c denotes the i-th window of the time series c. A functional network is then created for each window, composed of 32 nodes (one for each EEG sensor). The weight of the link between nodes j and k is then given by the absolute value of the Pearson's linear correlation between time series W Python software library. As a complement to this contribution, we make public a Python software library with the implementation of the methodology here proposed.
In order to preserve the open nature of the methodology, the library is structured in a flexible way, in which for instance the user can provide his/her own functions for reconstructing the functional networks and calculating topological metrics. The library is freely available at https:// gitlab. com/ MZanin/ netwo rk-featu re-stabi lity. We welcome readers to send us comments, suggestions and corrections, ideally using the "Issues" feature of GitLab.
Acknowledgements This work was supported by  TÜBİTAK  and  French Ministry of Foreign Affairs PHC-Bosphore Program  (respectively projects no:  218S314  and  42558YL ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No  851255 ). M. Z. acknowledges the  Spanish State Research Agency , through the  Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D  ( MDM-2017-0711 ).
study design This study includes two cross-sectional analyses integrating publicly available data using digital technologies and analytics. The study generated new knowledge of potential value when implementing evidence-informed approaches to improve accessibility and health equity. The study used updatable data to measure travel times and evaluate the effects of interventions and changes to infrastructure, service provision, traffic congestion and population. The following study methods seek to address current challenges for assessments of accessibility to health services 10 : ► Dynamic assessments of travel times to account for traffic variations. ► Inputs from diverse stakeholders to create an interactive platform that displays intersectoral data on dashboards so stakeholders can interpret data quickly and accurately.
Open access ► Disaggregated data to enable straightforward equity analysis of accessibility. ► Situational analysis of the accessibility to specific services for urban areas. ► An approach suitable for monitoring health equity related to accessibility. The data for the cross-sectional analyses were obtained from the internet-based AMORE Platform (https://www. iquartil. net/proyectoAMORE), hosted by iQuartil SAS, an analytics company, and developed under the leadership of the principal investigator (LGC). The AMORE Platform integrates data from: ► 2018 National Census Data for Cali, obtained from the official public databases of the Colombian National Department of Statistics (DANE in Spanish). 22  23 ► The administrative divisions of Cali were obtained from the Colombian IDESC Geoportal; Traffic Analysis Zones (TAZ) were matched to the census blocks.
13 30  The origin for each journey is the population-weighted centroid for the TAZ of the place of residence. Similarly, the destination is the centroid for the TAZ hosting the tertiary care emergency department with the shortest travel time. ► Google Distance Matrix API. For this baseline assessment of the Cali urban area, predicted travel times were downloaded on 3 July 2020 for the week of 6-12 July 2020, and on 27 October 2020, for the week of 23-29 November 2020. Travel times varied substantially during the COVID-19 pandemic and while it is unclear how this influenced Google Distance Matrix algorithms,  31  empirical and anecdotal reports suggest they remained accurate. ► The 14 tertiary care institutions with an emergency department in Cali were identified using REPS. 29  Databases were integrated and tested between August 2020 and October 2021 using: KNIME open-source data analytics reporting and integration platform, Python programming language software (back end) and an interface (front end) developed with interactive data visualisation software Microsoft PowerBi.
Patient and public involvement The AMORE Project Collaborative Group is diverse, with over two dozen contributors representing different stakeholders and sectors. Group members participated throughout the design, conduct and reporting of this study and the dissemination of results. The contributors and some public servants participated in the cocreation of knowledge and are listed in the Acknowledgements. These collaborators offer different governance perspectives: authorities, service providers, service users and organised civil society, including academics, advocates and experts from various fields of knowledge. 32 33  Data integration and output The AMORE Platform dashboards and visualisations provide descriptive indicators using simple maps, dials, bars and data. These indicators show travel times using the shortest journeys and descriptive statistics for each urban TAZ at a given traffic congestion level.
Open access The origin-destination times were estimated from the population-adjusted geographic centroid of each TAZ to the respective centroid where each institution was located. The Colombian census was completed through interviews with an adult for each household. Data were stored, linking it to a city block code to anonymise it. The AMORE Platform used the census microdata categorisations and, for a few variables, aggregate groups for simplification (eg, education was simplified with guidance from an expert in the Colombian education system, Psychologist Myriam Lorena Rosero Hernández, ME). In 2019, Colombia's National Administrative Department of Statistics (DANE in Spanish) recommended adjusting the 2018 population of Cali upward by 18% from the original census data due to under-registration.
23  35 DANE advised making further adjustments due to intercensal growth, migration and updates. 24 36  These adjustments amounted to 28. 1%, and DANE did not disaggregate the adjustment data. Therefore, 495 219 records were randomly selected and duplicated, reaching a population of 2 258 823 inhabitants, keeping the distributions. These are displayed in the AMORE Platform by toggling the census adjustment (figure  1 , 'Data type'). The right section of the platform displays sociodemographic characteristics (figure  1 ). The AMORE Platform displays the absolute and relative figures for the georeferenced data of inhabitants, both for the city or selected TAZs within a travel schedule figure  2. The variables integrated into the platform are listed in table 1.
We determined that nine clusters allowed us to discriminate traffic congestion based on sensitivity analysis to represent the 168 hours of the week. For each day and hour time band, we estimated the percentage difference between the minimum time of that trip and the travel for the time band. We calculated the average of this metric for all the journeys of the sample to obtain clusters. For example, the traffic from 18:00 to 19:00 on weekdays and from 12:00 to 14:00 on Saturdays behaves similarly for this cluster, representing the highest traffic congestion. Creating these clusters again reduces information requirements and costs. Using hourly assessments is arbitrary; travel times could be measured every minute or second. However, more frequent checks would increase costs (ie, from data downloads, computing time, people time) by orders of magnitude without substantive changes in the conclusions.
limitations The AMORE Platform used data modelling and clustering to estimate travel times between the origin and destination TAZ, lowering the cost of using big data and still delivering accessibility. Operational costs are thus low, and platform updates for monitoring and evaluation are affordable. The trade-off for affordability is imprecisions in estimates. These imprecisions are more likely to affect populations living near the 15 min threshold and those further away from TAZ centroids with heavy traffic congestion. However, these imprecisions are unlikely to change the overall urban assessment. Predicted travel times from the Google Distance Matrix API are accurate and are fed by big data from  Open access smartphones. Other databases, such as Waze Transport SDK API, can be used to generate estimates, but these providers do not release prediction algorithms.
Interpretation The AMORE Platform reveals accessibility and its health equity implications, providing new dynamic data that account for the effects of traffic. It does so more precisely and at a fraction of the costs of household surveys and origin-destination studies, providing a new tool to inform service plans, programmes and policies. Integrating publicly available data from public sources might be a breakthrough that improves evidenceinformed decisions regarding the location and provision of health services. Visualisations might help stakeholders interpret the data and agree on a common objective and metric: painting the city green by covering its entire population and offering equitable accessibility to all people. Updating the AMORE Platform is cheaper and faster than updating other origin-destination studies; its assessments are granular and sensitive to variations and can be used to monitor and evaluate changes.
Integrating evidence about accessibility and equity into stakeholder and intersectoral dialogues, decision-making processes and other strategies that seek the social appropriation of knowledge by stakeholders and sectors might be catalysts for implementation. 33 48  This report is part of the broader AMORE Project. Future reports will explore the potential of optimising the location of up to two new emergency departments. Future studies will assess the use and appropriation of data, and the advocacy of stakeholders, including those in the AMORE Project Collaborative Group (table 3). 10 49  Interactive platforms can help decision makers explore different assumptions and myriads of other results of combining different thresholds and traffic levels, allowing data to speak for itself. 18 50  Looking ahead, identifying and proposing public policy plans and partnerships could improve health equity and bring hope to residents of Cali.
Conclusions and future directions These results test an approach to provide a situational analysis. Defining potential improvements by adding services and using data by decision makers and other stakeholders are part of the broader AMORE Project and the subject of future reports. Travel time is a continuous variable and could be the subject of myriad analyses beyond the purpose of this study. Our aim is to demonstrate the possibility of conducting an affordable situational analysis with existing data providing information that decision makers and other stakeholders might use, something to be assessed in future studies. The web-based platform allows users to change assumptions or variables, explore different scenarios or perform sensitivity analyses. The possibilities are numerous and include expanding the study with more bivariate and multivariate analyses that go beyond the objectives of this report.