Usage of high-level intermediate representations promises the generation of fast code from a high-level description, improving the productivity of developers while achieving the performance traditionally only reached with low-level programming approaches.

High-level IRs come in two flavors: 1) domain-specific IRs designed to express only for a specific application area; or 2) generic high-level IRs that can be used to generate high-performance code across many domains. Developing generic IRs is more challenging but offers the advantage of reusing a common compiler infrastructure various applications.

In this paper, we extend a generic high-level IR to enable efficient computation with sparse data structures. Crucially, we encode sparse representation using reusable dense building blocks already present in the high-level IR. We use a form of dependent types to model sparse matrices in CSR format by expressing the relationship between multiple dense arrays explicitly separately storing ...

This map illustrates the spatial distribution of tree density across the governorate within Al Bahah Region, Kingdom of Saudi Arabia. Tree density is expressed as the number of trees per square kilometer and is categorized into five density classes, ranging from sparse to dense coverage. Data were derived from the National Center for Vegetation Cover (NCVC) using field surveys, remote sensing imagery, and GIS analysis. The map supports forestry management, ecological monitoring, and natural resource planning at the sub-regional level.

Species and environmental dataThis compiled (zip) file consists of 7 matrices of data: one species data matrix, with abundance observations per visited plot; and 6 environmental data matrices, consisting of land cover classification (Class), simulated EnMAP and Landsat data (April and August), and a 6 time-step Landsat time series (January, March, May, June, July and September). All data is compiled to the 125m radius plots, as described in the paper.Leitaoetal_Mapping beta diversity from space_Data.zip,1. Spatial patterns of community composition turnover (beta diversity) may be mapped through Generalised Dissimilarity Modelling (GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional datasets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing datasets, including a time series of Landsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformed GDM models when fit on high-dimensional datasets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single date multispectral imagery. 4. This approach improved the direct use of high-dimensional remote sensing data, such as time series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional datasets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity.

The dataset consists of geospatial files depicting the estimated June-to-November distribution of walrus foraging and occupancy during a four year period of sparse sea ice cover above the Chukchi Sea continental shelf (2008-2011). The walrus distribution and utilization estimates are based on location data from satellite-linked radio-tracked walruses in the Chukchi Sea (2008-2011). Compared to previous years this period was marked by earlier and more extensive sea ice retreat (June - September) and delayed sea ice freeze-up (October - November). This allowed walruses to arrive earlier, occupy slightly more northern areas, and stay later in the Chukchi Sea than previously. Data are combined for all four years and structured by month (June [06] - November [11]). Within each month, we estimated walrus utilization distributions by using an unweighted movement based kernel density estimator. The kernel density was then weighted by the amount of time walruses spent foraging during each recorded track segment. We present contours of the kernel density ranging from the 10th to 95th percentile.

Data from: Data-driven analysis of oscillations in Hall thruster simulations

• Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino

• Contact email: dmaddalo@ing.uc3m.es

• Date: 2022-03-24

• Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis

• Version: 1.0.4

• Digital Object Identifier (DOI): 10.5281/zenodo.6359505

• License: This dataset is made available under the Open Data Commons Attribution License

Abstract

This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication:

Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 Plasma Sources Sci. Technol. 31:045026. Doi: 10.1088/1361-6595/ac6444.

Additionally, the raw simulation data is also employed in the following journal publication:

Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 Mach. Learn.: Sci. Technol. 5:035057. Doi: 10.1088/2632-2153/ad6d29

Dataset description

The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code HYPHEN, while the HODMD results have been produced using an adaptation of the original HODMD algorithm with an improved amplitude calculation routine.

Please refer to the relative article for further details regarding any of the parameters and/or configurations.

Data files

The data files are in standard Matlab .mat format. A recent version of Matlab is recommended.

The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as:

"n" for plasma density

"Te" for electron temperature

"phi" for plasma potential

"ji" for ion current density (both single and double charged ones)

"nn" for neutral density

"Ez" for axial electric field

"Si" for ionization production term

"vi1" for single charged ions axial velocity

In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables.

The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.

Citation

Works using this dataset or any part of it in any form shall cite it as follows.

The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.

Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.

Acknowledgments

This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.

The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Sites were chosen in uniform stands of aspen or spruce. The dominant species in the site constituted over 80 percent, and usually over 95 percent, of the total tree density and basal area. Aspen stands were chosen to represent the full range of age and stem density of essentially pure aspen, of nearly complete canopy closure, and greater than two meters in height. Spruce stands ranged from very sparse stands on bog sites, to dense, closed stands on more productive peatlands. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Within each plot, all woody stems greater than two meters in height were recorded by species and the following dimensions were measured: diameter breast height, height of the tree, height of the first live branch, and depth of crown. For each plot, a two meter diameter subplot was defined at the center of each plot. Within this subplot, the percent of ground coverage by plants under one meter in height was determined by species. These data, averaged for the five plots in each site, are presented in this data set (i.e., SNF Forest Understory Cover Data (Table)) in tabular format, e.g. plant species with a count for that species at each site. The same data are presented in the SNF Forest Understory Cover Data data set but are arranged with a row for each species and site and a percent ground coverage for each combination.

This map presents the spatial distribution of tree basal area (m²/ha) across Baljurashi Governorate. Basal area values were derived from field-measured diameter at breast height (DBH) data and calculated per plot using standard forestry equations. The dataset highlights variations in stand density and forest structure, with mapped averages ranging from sparse to dense tree cover. The data supports biomass estimation, forest monitoring, and land management decisions by providing a measure of stand occupancy and growth potential.

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function.

This dataset details the spatial distribution of tree density specifically within Baljurashi Governorate, part of the Al Bahah Region in Saudi Arabia. It quantifies tree coverage as the number of trees per square kilometer, classified into density categories from sparse to dense. Developed by the FAO in collaboration with the National Center for Vegetation Cover (NCVC), this map integrates field surveys, remote sensing, and GIS analysis. It serves as a critical tool for local forestry initiatives, ecological assessment, and sustainable resource management within the governorate.

Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSHA) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:

(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at https://doi.org/10.5067/GHMWO-4FR05. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.

(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at https://doi.org/10.5067/AQR50-4U7CS. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day.

(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at https://doi.org/10.5067/SLREF-CDRV2. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid

(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.

Preprocessing code can be found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py.

SSS, SST, SSH were regridded and resampled onto a 1/2 degree lat/lon 7day grid. Data, along with anomalies, is contained in the file sss_sst_ssh_anomalies.nc.

Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies are stored in mldb_climatology_climatologystd_binned.nc. Meanwhile, mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc contains contains the mld, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks.

All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/. See https://github.com/NCAR/ml-ocean-bl/notebooks for use cases and examples.

Contact D. Foster with any questions.

This is a publication of the CoAID dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents. Cui, Limeng, et Dongwon Lee. 2020. « CoAID: COVID-19 Healthcare Misinformation Dataset ». ArXiv:2006.00885 [Cs], novembre. http://arxiv.org/abs/2006.00885. In this dataset, we provide multiple features extracted from the text itself. Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications. Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (features_news) [1] - A corpus of tweets reporting news for TF-IDF weighting. (features_tweets) [1] - A S-BERT model [2] that uses distiluse-base-multilingual-cased-v1 (called features_use) [3] - A S-BERT model [2] that uses paraphrase-multilingual-mpnet-base-v2 (called features_mpnet) [4] References: [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2

The clear boundary between urban and rural areas is gradually disappearing, and urban and rural areas are two poles of a gradient with many continuous human settlements in between, which is a concept known as the rural–urban continuum. Little is known about the distribution and change trajectories of the various types in the rural–urban continuum across the globe. Therefore, using global land-cover data (FROM-GLC Plus) and global population data (Worldpop) based on the decision-making tree method, this study proposed a method and classification system for global rural–urban continuum mapping and produced the mapping results on a global scale in the Google Earth Engine platform. With the expansion of built-up areas and the increase in population, the global human settlements follow the pattern that develops from wildland to villages (isolated—sparse—dense), and then to towns (sparse—dense), and finally to urban areas (edge—center). From a regional perspective, there are some obvious differences: Africa is dominated by sparse villages; Asia has the highest proportion of densely clustered towns; the proportion of dense villages in Europe is high. Rural–urban continuum mapping and analysis provide a database and new insights into urbanization and differences between urban and rural areas around the world.

This data release includes grids representing the depth and thickness of drinking-water withdrawal zones, polygons of hydrogeologic settings, an inventory of sources of well construction data, and summaries of data comparisons used to assess the depth of groundwater used for drinking-water supplies in the United States. Well construction data sources are documented in Table1_DataSources.xlsx. Data comparisons using the Mann-Whitney test to assess similarity between hydrogeologic settings were used to justify combining data where they were sparse (compare_neighbors_all_domestic.txt and compare_neighbors_all_public.txt). Water-supply-well depth varies geographically by water use and the type of well, which illustrates the need to identify the depth of domestic drinking water withdrawal and depth of public supply drinking water withdrawal zones. Water-supply-well depth also varies by aquifer; therefore median values were calculated for each Principal Aquifer (PA), Secondary Hydrogeologic Region (SHR) between PAs and PA or SHR associated with overlying sediment polygons, where present, including glacial (G), coarse glacial (GC), and stream-valley alluvium (AV) polygons (all termed hydrogeologic settings here). A polygon shape file of hydrogeologic settings is included in this data release (HG_Settings.zip) and includes well counts and median thicknesses and depths for each area. This data release documents an inventory of well construction data sources and thickness and median top and bottom of drinking water depth zones by aquifer for domestic and public supplies. This data release includes equations used for estimating information for wells missing information on the depth to the top/length of the open interval. This data release contains: HG_Settings.zip --Shape file with well counts and median depth and thickness for hydrogeologic setting areas compare_neighbors_all_domestic.txt --results of Mann-Whitney tests to assess domestic-supply well construction data similarity between hydrogeologic settings compare_neighbors_all_public.txt --results of Mann-Whitney tests to assess public-supply well construction data similarity between hydrogeologic settings Depth_of_Drinking_Water_Supplies_Metadata03162021.xml --Metadata NonReferencedDomestic.txt --An inventory of domestic-supply well data that are not published elsewhere NonReferencedPublic.txt --An inventory of public-supply well data that are not published elsewhere Table1_DataSources.xlsx --An inventory of databases, references, state web sites, and individual state contacts for data sources. The logic behind data extraction algorithms is also defined for each data source. A tab delimited text version with the same name is also available. Lithology_OpenIntervalLengthFit.txt --Parameters for equations used for estimating open intervals by lithology, overlying sediment, and well type HydrogeologicSetting_OpenIntervalLengthFit.txt --Parameters for equations used for estimating open intervals by hydrogeologic setting and well type domestic_grids.zip contains: domestic_bottom_dist_to_5.asc --Grid of domestic-supply well open interval bottom depth data density, distance to reach 5 wells with information on the bottom of the open interval domestic_open_dist_to_5.asc --Grid of domestic-supply well open interval length data density, distance to reach 5 wells with information on open interval length domestic_bottom_open.asc --Grid of domestic-supply well depth to the bottom of the open interval domestic_len_open.asc --Grid of domestic-supply well open-interval length domestic_top_open.asc --Grid of domestic-supply well depth to the top of the open interval public_grids.zip contains: public_bottom_dist_to_5.asc --Grid of the public-supply well open interval bottom depth data density, distance to reach 5 wells with information on the bottom of the open interval public_open_dist_to_5.asc --Grid of the public-supply well open interval length data density, distance to reach 5 wells with information on open interval length public_bottom_open.asc --Grid of the public-supply well depth to the bottom of the open interval public_len_open.asc --Grid of the public-supply well open-interval length public_top_open.asc --Grid of the public-supply well depth to the top of the open interval pubdom_difference_grids.zip contains: pubdom_bottom_open_diff.asc --Grid of the differences between the public-supply and domestic-supply well moving median surfaces representing the bottom of the open interval pubdom_len_open_diff.asc --Grid of the differences between the public-supply and domestic-supply well moving median surfaces representing the length of the open interval pubdom_top_open_diff.asc --Grid of the differences between the public-supply and domestic-supply well moving median surfaces representing the top of the open interval

We show that every first-order property of graphs can be decided in almost linear time on every nowhere dense class of graphs. For graph classes closed under taking subgraphs, our result is optimal (under a standard complexity theoretic assumption): it was known before that for all classes C of graphs closed under taking subgraphs, if deciding first-order properties of graphs in C is fixed-parameter tractable, parameterized by the length of the input formula, then C must be nowhere dense. Nowhere dense graph classes form a large variety of classes of sparse graphs including the class of planar graphs, actually all classes with excluded minors, and also bounded degree graphs and graph classes of bounded expansion. For our proof, we provide two new characterisations of nowhere dense classes of graphs. The first characterisation is in terms of a game, which explains the local structure of graphs from nowhere dense classes. The second characterisation is by the existence of sparse neighbourhood covers. On the logical side, we prove a rank-preserving version of Gaifman's locality theorem. The characterisation by neighbourhood covers is based on a characterisation of nowhere dense classes by generalised colouring numbers. We show several new bounds for the generalised colouring numbers on restricted graph classes, such as for proper minor closed classes and for planar graphs. Finally, we study the parameterized complexity of the first-order model-checking problem on structures where an ordering is available to be used in formulas. We show that first-order logic on ordered structures as well as on structures with a successor relation is essentially intractable on nearly all interesting classes. On the other hand, we show that the model-checking problem of order-invariant monadic second-order logic is tractable essentially on the same classes as plain monadic second-order logic and that the model-checking problem for successor-invariant first-order logic is tractable on planar graphs.

Sparse LiDAR Densification Software Market Outlook

According to our latest research, the global Sparse LiDAR Densification Software market size reached USD 315.2 million in 2024, driven by increasing adoption of LiDAR technology across various industries. The market is expected to grow at a robust CAGR of 18.6% during the forecast period, reaching a projected value of USD 1,518.7 million by 2033. This growth is primarily fueled by the rising demand for high-fidelity 3D mapping, the proliferation of autonomous vehicles, and the need for precise environmental perception in robotics and smart city infrastructure. As per our latest research, the market is witnessing a rapid technological evolution, with significant investments from both established players and emerging startups.

One of the key growth factors propelling the Sparse LiDAR Densification Software market is the increasing integration of LiDAR sensors in autonomous vehicles and advanced driver-assistance systems (ADAS). As the automotive industry accelerates its shift toward autonomy, the need for accurate and dense point cloud data has become paramount. Sparse LiDAR datasets, while cost-effective, often lack the resolution required for complex perception tasks. Densification software bridges this gap by enhancing the quality and density of LiDAR data, enabling safer navigation, improved obstacle detection, and precise localization. This trend is further reinforced by regulatory mandates and safety standards that require robust sensor fusion and perception capabilities in next-generation vehicles, thereby driving the adoption of densification solutions.

Beyond automotive applications, the market is experiencing substantial growth due to the expanding use of LiDAR in robotics, industrial automation, and smart city initiatives. In robotics, dense 3D mapping is essential for navigation, manipulation, and interaction with dynamic environments. Sparse LiDAR Densification Software enables robots to operate efficiently in cluttered or unstructured settings by providing enhanced spatial awareness. Similarly, in smart city projects, the software supports urban planning, infrastructure monitoring, and traffic management by delivering high-resolution geospatial data. The construction, agriculture, and transportation sectors are also leveraging densified LiDAR data for tasks such as site surveying, crop monitoring, and logistics optimization, further broadening the market’s scope.

Another significant driver is the rapid advancement in artificial intelligence (AI) and machine learning (ML) algorithms, which are increasingly being embedded in densification software solutions. These technologies enable real-time processing and upscaling of sparse point clouds, reducing computational overhead and improving scalability. The availability of cloud-based deployment models has democratized access to high-performance densification tools, allowing small and medium enterprises to leverage advanced LiDAR analytics without substantial upfront investments in hardware. Additionally, partnerships between software vendors, LiDAR hardware manufacturers, and industry-specific solution providers are fostering innovation and accelerating market penetration across diverse verticals.

From a regional perspective, North America leads the global Sparse LiDAR Densification Software market, accounting for the largest revenue share in 2024. This dominance is attributed to early adoption of autonomous technologies, strong presence of leading automotive and tech companies, and significant investments in smart infrastructure. Europe follows closely, driven by stringent regulatory frameworks and a robust ecosystem of LiDAR technology providers. The Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, expanding industrial automation, and government-led smart city initiatives. Latin America and the Middle East & Africa are gradually catching up, with increasing investments in infrastructure development and digital transformation projects.

Component Analysis

This map illustrates the spatial distribution of tree density across the governorates within Al Bahah Region, Kingdom of Saudi Arabia. Tree density is expressed as the number of trees per square kilometer and is categorized into five density classes, ranging from sparse to dense coverage. Data were derived from the National Center for Vegetation Cover (NCVC) using field surveys, remote sensing imagery, and GIS analysis. The map supports forestry management, ecological monitoring, and natural resource planning at the sub-regional level.

Global measurements of ocean pCO2 are critical to monitor and understand changes in the global carbon cycle. However, pCO2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on machine learning, have been used to upscale and extrapolate sparse point data to dense global estimates based on globally available input features. However, those estimates tend to exhibit spatially heterogeneous performance. As a result, we propose a physics-informed transfer learning workflow to generate dense pCO2 estimates. The model is initially trained on synthetic Earth system model data, and then adjusted (using transfer learning) to the actual sparse SOCAT observational data, thus leveraging both the spatial and temporal correlation pre-learned on physically-informed Earth system ensembles. Compared to the benchmark upscaling of SOCAT point-wise data on baseline models, our transfer learning methodology shows a major improvement of up to 30-52%. Our strategy thus presents a new monthly global pCO2 estimates that spans for 35 years between 1982 and 2017.

This data set comes from the National Institute of Forests (INAB) in Guatemala. Through various joint efforts and in coordination with the Inter-institutional Group for Forest Monitoring and the GIZ, INAB obtained 308 high resolution RapidEye (RE) images to cover the entire country. These images, with a spatial resolution of 5 meters multispectral, were used to detail 16 classes of forest, 21 subtypes of forest, and 16 subtypes of forest by density. For broadleaf, coniferous, and mixed forest, detailed densities (sparse and dense) were differentiated for the first time in Guatemala.Mangroves were identified at the species level thanks to the database of Project Mangrove, 2012 MARN-CATHALAC, which has registers of four species. For the purposes of this map, un-forested zones were simply designated “No Forest”."

This is a publication of the FibVid dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents. Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». Telematics and Informatics 64 (novembre): 101688. https://doi.org/10.1016/j.tele.2021.101688. In this dataset, we provide multiple features extracted from the text itself. Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications. Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (features_news) [1] - A corpus of tweets reporting news for TF-IDF weighting. (features_tweets) [1] - A S-BERT model [2] that uses distiluse-base-multilingual-cased-v1 (called features_use) [3] - A S-BERT model [2] that uses paraphrase-multilingual-mpnet-base-v2 (called features_mpnet) [4] References: [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2

Sensory systems adapt their response properties to the statistics of their inputs. For instance, visual systems adapt to low-order statistics like mean and variance to encode stimuli efficiently or to facilitate specific downstream computations. However, it remains unclear how other statistical features affect sensory adaptation. Here, we explore how Drosophila’s visual motion circuits adapt to stimulus sparsity, a measure of the signal’s intermittency not captured by low-order statistics alone. Early visual neurons in both ON and OFF pathways alter their responses dramatically with stimulus sparsity, responding positively to both light and dark sparse stimuli but linearly to dense stimuli. These changes extend to downstream ON and OFF direction-selective neurons, which are activated by sparse stimuli of both polarities, but respond with opposite signs to light and dark regions of dense stimuli. Thus, sparse stimuli activate both ON and OFF pathways, recruiting a larger fraction of the ..., This dataset contains all experimental data necessary to create figures in Tong et al. (2024), as well as scripts to analyze them. The scripts are written in Matlab 2021b, and uses some functions from Statistics and Machine Learning Toolbox., , # Data from: Adaptation to visual sparsity enhances responses to isolated stimuli

https://doi.org/10.5061/dryad.t1g1jwtbs

Description of the data and file structure

This repository contains the data and analysis scripts for the paper "Adaptation to visual sparsity enhances responses to isolated stimuli."

• sparsity_Dryad_upload.zip: Complete dataset archive containing all data and scripts. Unzip it, and all the main analysis and plotting can be reproduced using the provided scripts and data.

Folder structure

• scripts/: MATLAB scripts for data analysis and figure generation
• data/: .mat data files required by the scripts
• utilities/: helper functions used by the analysis scripts

scripts/: Each script reproduces the indicated figure panels from the paper (and supplements) by loading the appropriate data from data/

• fig1_naturalScene.m: Fig 1A‑B, S1A‑D
• fig1_Mi1GC6f_flash.m: Fig 1G‑N, S2F‑L
• `fig2_con...,