The Swedish Contextual Database provides a large number of longitudinal and regional macro-level indicators primarily assembled to facilitate research on the effects of contextual factors on family and fertility behavior. It can be linked to the individual-level data of the Swedish GGS as well as to data of other surveys. It can also be used for other types of research and for teaching. The comparative data will also be integrated into the international Contextual Database of the GGP. The contextual data are available open-access through the GGP webpage: www.ggp-i.org and through the webpage of Stockholm University Demography Unit www.suda.su.se

Purpose:

The Swedish contextual database (CDB) was established to accompany the Swedish Generations and Gender Survey (GGS) and to complement the contextual database of the international Generations and Gender Programme (GGP).

The Swedish Contextual Data Collection is available in xls format. In addition to that, the internationally comparative data will be integrated into the Contextual Database (CDB) of the GGP in 2018. These data can be exported in other formats, as well (e.g. CSV, XML). The indicators can also be accessed in a single file in STATA or SPSS format. The data can be matched with the Swedish GGS. International regional coding schemes are also supported, such as NUTS, OECD.

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for all years allow users to visualize flight line locations using GIS software.

Sample names, sampling descriptions and contextual data.

Contextual Master Data Management Market Outlook

According to our latest research, the global Contextual Master Data Management (MDM) market size reached USD 13.2 billion in 2024, reflecting robust demand across sectors. The market is expected to expand at a CAGR of 16.7% from 2025 to 2033, with the forecasted market size projected to reach USD 44.5 billion by 2033. This remarkable growth is primarily driven by the increasing need for real-time, context-aware data management solutions that help organizations enhance operational efficiency, improve decision-making, and ensure regulatory compliance in an increasingly complex digital landscape.

The primary growth factor fueling the Contextual Master Data Management market is the exponential rise of data volumes generated by organizations worldwide, particularly with the proliferation of IoT devices, cloud applications, and digital transformation initiatives. As businesses strive to leverage data for gaining actionable insights, the need for accurate, unified, and contextually relevant master data has become paramount. Contextual MDM solutions enable organizations to harmonize disparate data sources, providing a single source of truth that is enriched with contextual information such as location, time, and user behavior. This capability is especially critical in sectors like healthcare, retail, and BFSI, where personalized customer experiences and regulatory compliance hinge on the quality and context of master data. The increasing complexity of data ecosystems and the growing importance of data-driven strategies are expected to sustain high demand for contextual MDM solutions throughout the forecast period.

Another significant driver of the Contextual Master Data Management market is the rapid adoption of cloud-based technologies and the integration of artificial intelligence (AI) and machine learning (ML) into MDM platforms. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it an attractive option for organizations of all sizes. AI and ML capabilities further enhance contextual MDM by automating data classification, improving data quality, and enabling predictive analytics. These advancements allow organizations to proactively manage data anomalies, identify relationships between data entities, and deliver more personalized services to customers. The synergy between cloud computing, AI, and contextual MDM is expected to unlock new opportunities for innovation and efficiency across industries, driving sustained market growth.

The growing emphasis on regulatory compliance and data governance is also propelling the Contextual Master Data Management market forward. Organizations are under increasing pressure to comply with stringent data privacy regulations such as GDPR, CCPA, and industry-specific mandates. Contextual MDM solutions help organizations maintain data integrity, traceability, and auditability, reducing the risk of non-compliance and associated penalties. Furthermore, as organizations expand their global footprint and engage in cross-border operations, the need for consistent and contextually accurate master data becomes even more critical. The ability to manage data in compliance with regional and industry-specific regulations is a key differentiator for contextual MDM solutions, further contributing to their adoption across diverse sectors.

From a regional perspective, North America continues to dominate the Contextual Master Data Management market, accounting for the largest revenue share in 2024. This leadership is attributed to the high concentration of technology-driven enterprises, early adoption of advanced data management solutions, and strong regulatory frameworks. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing investments in cloud infrastructure, and the rising adoption of AI-powered data management tools. Europe also demonstrates significant growth potential, particularly in sectors such as BFSI, healthcare, and manufacturing, where data quality and compliance are top priorities. The Middle East & Africa and Latin America regions are gradually catching up, supported by government-led digital initiatives and growing awareness of the benefits of contextual MDM.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this research was to examine the influence of neighborhood social disorganization on the risk of homicide victimization, with focus on how community effects changed once individual-level characteristics were considered. This research integrated concepts from social disorganization theory, a neighborhood theory of criminal behavior, with concepts from lifestyle theory and individual theory of criminal behavior, by having examined the effects of both neighborhood-level predictors of disadvantage and individual attributes which may compel that person to behave in certain ways. The data for this secondary analysis project are from the 2004-2012 National Center for Health Statistics' (NCHS) National Health Interview Survey (NHIS) linked National Death Index-Multiple Causes of Death (MDC) data, which provided individual-level data on homicide mortality. Neighborhood-level (block group) characteristics of disadvantage that existed within each respondent's place of residence from the 2005-2009 and 2008-2012 American Community Surveys were integrated using restricted geographic identifiers from the NHIS. As a syntax-only study, data included as part of this collection includes 38 SAS Program (syntax) files that were used by the researcher in analyses of external restricted-use data. The data are not included because they are restricted archival data from the NHIS from the Centers for Disease Control and Prevention combined with publicly available American Community Survey (ACS) block group level data.

Contextual Intelligence Platforms Market Outlook

According to our latest research, the global Contextual Intelligence Platforms market size reached USD 4.7 billion in 2024, reflecting a robust expansion driven by the rapid adoption of artificial intelligence and data-driven decision-making across various sectors. The market is expected to grow at a CAGR of 22.5% from 2025 to 2033, reaching a projected value of USD 36.6 billion by 2033. This strong growth trajectory is primarily attributed to the increasing demand for advanced analytics solutions that enable organizations to derive actionable insights from contextual data, enhancing operational efficiency and customer engagement.

The primary growth factor propelling the Contextual Intelligence Platforms market is the exponential increase in data generation from digital channels, IoT devices, and enterprise systems. Organizations across industries are seeking sophisticated solutions that can process and analyze vast amounts of structured and unstructured data in real time. Contextual intelligence platforms leverage machine learning, natural language processing, and advanced analytics to provide deep insights that are contextually relevant, empowering businesses to make informed decisions swiftly. The surge in digital transformation initiatives, particularly in sectors such as BFSI, healthcare, and retail, is further accelerating the adoption of these platforms. Enterprises are increasingly recognizing the value of contextual data in personalizing customer experiences, optimizing operations, and mitigating risks, thereby fueling market growth.

Another significant driver is the growing emphasis on enhancing customer experience and personalization. In today’s highly competitive landscape, businesses are leveraging contextual intelligence to deliver tailored products, services, and interactions that resonate with individual customer preferences and behaviors. By integrating contextual intelligence platforms with CRM, marketing automation, and customer support systems, organizations can gain a 360-degree view of their customers, anticipate needs, and proactively address issues. This capability not only improves customer satisfaction and loyalty but also drives revenue growth through targeted marketing and upselling opportunities. The increasing adoption of omnichannel strategies and the proliferation of digital touchpoints are further amplifying the demand for contextual intelligence solutions.

A third key factor contributing to the growth of the Contextual Intelligence Platforms market is the rising concern over security, fraud detection, and risk management. With the escalation of cyber threats and regulatory requirements, organizations are turning to contextual intelligence to enhance their security posture and ensure compliance. These platforms enable real-time monitoring and analysis of user behavior, transactions, and network activities, allowing for the early detection of anomalies and potential threats. The ability to contextualize and correlate data from multiple sources significantly improves the accuracy and effectiveness of risk management processes, making contextual intelligence an indispensable tool for organizations operating in highly regulated and risk-prone sectors.

From a regional perspective, North America continues to dominate the Contextual Intelligence Platforms market, accounting for the largest share in 2024. The region’s leadership is underpinned by the presence of major technology vendors, a mature digital ecosystem, and substantial investments in AI and analytics. Europe and Asia Pacific are also witnessing significant growth, driven by increasing digitalization, regulatory mandates, and the adoption of advanced analytics in emerging economies. The Asia Pacific region, in particular, is expected to register the highest CAGR during the forecast period, fueled by rapid economic development, expanding internet penetration, and the proliferation of smart devices. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and investments in data-driven technologies.

Component Analysis

The Contextual Intelligence Platforms market is segmented by component into software and services, each playing a critical role in the overall ecosystem. The software segment currently holds the largest market share, driven by the continuous evolution of AI algorithms, machine learning models, and analytics engines. These software solut

Perception is context dependent. For example, the perceived orientation of a bar changes depending on the presence of oriented bars around it. Contextual effects have also been demonstrated for more complex judgements, such as facial attractiveness or expression, although it remains unclear how these contextual facial effects depend on the types of faces surrounding the target face.To examine this, we measured the perceived age (a quantifiable measure) of a target face in the presence of differently aged faces in the surround. Using a unique database of standardized passport photos, participants were asked to estimate the age of a target face which was viewed either on its own or surrounded by two different identity flanker faces. The flanker faces were either both younger or both older than the target face, with different age offsets between flankers and targets of ±5, ±10, ±15, ±20 years. We find that when a target face is surrounded by younger faces, it systematically appears younger...

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. Sexual and Gender Minority measures in this release include county-level summary data on the proportion of same-sex households in the United States, as reported in the 2020 Decennial Census. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from these contexts to help processing the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared to unexpected stimuli. However, most of these results concern expectations derived from temporal (sequential) regularities. Thus, little is known about the neural consequences of statistical learning of spatial regularities. In the current fMRI study, thirty-three human volunteers were exposed to object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found a reliable modulation of neural responses by both, spatial and temporal context. Specifically, neural responses to stimuli in expected compared to unexpected contexts were suppressed throughout the ventral visual stream. Interestingly, the modulation by spatial context was stronger in magnitude and more reliable than modulations by temporal context. These results suggest that while both, spatial and temporal context priors modulate sensory processing in a similar fashion, predictions of spatial context may be a more powerful modulator in the visual system.

This Data Sharing Collection contains all relevant data accompanying the manuscript "Preparatory attention incorporates contextual expectations" (raw data, experiment scripts, stimuli, processed-data, analysis scripts, manuscript and supplements, including README files). Note: anatomical images are defaced for privacy reasons.

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

This dataset is unique in three aspects:

• The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.
• It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.
• The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences

During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms.

Robot@Home2

Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.

If you use Robot@Home2, please cite the following paper:

Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101490.

@article{ambrosio2023robotathome2,
title = {The Robot@Home2 dataset: A new release with improved usability tools},
author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez},
journal = {SoftwareX},
volume = {23},
pages = {101490},
year = {2023},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2023.101490},
url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863},
keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab}
}

Version history
v1.0.1 Fixed minor bugs.
v1.0.2 Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version.
v2.0.0 SQL based dataset. Robot@Home v1.0.2 has been packed into a sqlite database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the toolbox.
v2.0.1 A forgotten foreign key pair have been added.
v.2.0.2 The views have been consolidated as tables which allows a considerable improvement in access time.
v.2.0.3 The previous version does not include the database. In this version the database has been uploaded.
v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.

This table presents data from the article Ineffective Cues for Contextual Saccade Adaptation by M. Martel & L. Madelain. https://doi.org/10.1152/jn.00148.2025 Contextual saccadic adaptation was investigated using a variant of the double-step paradigm, in which two directions of intra-saccadic steps were associated with two different contextual cues—allowing the simultaneous induction of two distinct saccadic adaptations. We tested eleven different contextual cues to signal intra-saccadic steps in this paradigm: • target color and shape, • target color and shape and perceptual report, • visual stimulus duration, • amplitude of the first step, • starting location of the target, • symbolic cues, • symbolic cues and perceptual report, • lateralization of a sound, • statistical regularities across trials (Block 1, Block 4, Block10). The dataset includes saccade angle measurements from the adaptation and late-learning phases, along with their variability and the statistical results presented in Figures 3, 4, and 5. It also contains the group-level statistics corresponding to Figure 6.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Chogha Mish Fauna" data publication.

Groundwater ecosystems are globally widespread yet still poorly understood. We therefore investigated contextual and environmental data characterizing 138 groundwater samples from Canadian groundwater ecosystems. The chemical, physical, gas, isotopic, and microbiological measurements were taken from aquifers in the Canadian Prairie between 2015 and 2020. The study area comprised 14 major aquifers and a geographic area of ~210.000 km2. The goal of the study was to understand the links between the biogeochemistry and microbial ecology of groundwater ecosystems in diverse geological settings on a broad spatial scale. Details concerning methods, results and conclusions can be found in the associated publication by Ruff et al. 2023.

Test data for the unrestricted viewing conditionThis file contains all bee choices for the 20 choices of each test (learning, transfer 1 and transfer 2) for bees in the unrestricted viewing condition (rotating screen apparatus).Screen_test_data.csvLearning phase data for the restricted and unrestricted viewing conditionsThis file contains all choices for each bee during the 80 conditioned choices of the learning phase for bees trained under both restricted and unrestricted viewing conditions.Learning_phase.csvTest data for the restricted viewing conditionThis file contains the 20 choices each bee made during all tests (learning, transfer 1 and transfer 2) in the restricted viewing condition using the y-maze.Ymaze_test_data.csv

This zip file contains 21 raster layers representing data from a variety of landscape metrics used to analyze the landscape context of Bighorn Canyon National Recreation Area (BICA). Their names, descriptions and categorization are as follows: Housing This raster dataset contains sixteen layers named in the format "bhc1950us," with the name of each layer containing a year representing the decades from 1950 through 2100 (ex. bh1960us, bhc1970us, bhc1980us, etc.). The layers depict housing density classes for the area around the 30 km buffer around and including BICA’s managed lands for each decade. These housing density estimates come from a Spatially Explicit Regional Growth Model (SERGoM, Theobald 2005) based on U.S. Census data from 2010 and depict the location and density of private land housing unit classes around BICA. SERGoM methods combined housing data with information on land ownership and density of major roads (interstates, state highways, and county roads) to provide a more accurate allocation of the location of housing units over the landscape. Details on how SERGoM was used for NPS data can be found in the NPScape Standard Operating Procedure (SOP): Housing Measure at https://irma.nps.gov/DataStore/Reference/Profile/2221576 The SERGoM used historical and current housing density patterns as data inputs to develop a simulation model to forecast future housing density patterns based on county-level population projections. Further details about the methodology of SERGoM can be found at https://www.jstor.org/stable/26267722?seq=2 SERGoM_bhc_metrics: Value CLASSNAME 0 Private undeveloped 1 2,470 units / square km 12 Commercial/industrial Land Cover This raster dataset depicts land cover and contains four layers from the National Land Cover Database (NLCD). The names and descriptions of each layer are as follows: NLCD2001. The National Land Cover Database 2001 land cover layer for mapping was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. This is a single layer for 2001 landcover data at all levels. Level 2 data for 2001 can be derived from this layer by collapsing level 1 features into level 2 categories. This level 1 layer contains seventeen classes: Value Land Cover 0 Unknown 11 Open Water 12 Perennial Snow/Ice 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands NLCD2001 land cover class descriptions: Open Water - All areas of open water, generally with less than 25% cover or vegetation or soil. Perennial Ice/Snow - All areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover. Developed, Open Space - Includes areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20 percent of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes. Developed, Low Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20-49 percent of total cover. These areas most commonly include single-family housing units. Developed, Medium Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50-79 percent of the total cover. These areas most commonly include single-family housing units. Developed, High Intensity - Includes highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80 to100 percent of the total cover. Barren Land - Rock/Sand/Clay; Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover. Deciduous Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species shed foliage simultaneously in response to seasonal change. Evergreen Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage. Mixed Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 percent of total tree cover. Shrub/Scrub - Areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions. Herbaceous - Areas dominated by graminoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling but can be utilized for grazing. Hay/Pasture - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation. Cultivated Crops - Areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class also includes all land being actively tilled. Woody Wetlands - Areas where forest or shrub land vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Landcover_NaturalConverted_NLCD2011. This layer depicts natural vs. converted land cover circa 2011 and was extracted from the map package NLCD2011_LNC.mpk. This layer contains two classes: Value Class Name 1 Converted 2 Natural Natural vs. Converted class descriptions: Converted - Developed areas, cultivated crops, and hay/pasture lands. Natural - All other major cover types. Landcover_Level1_NLCD2011. This layer was extracted from the map package NLCD2011_Level1.mpk and contains nine classes: Value Class Name 1 Open Water 2 Developed 3 Barren/Quarries/Transitional 4 Forest 5 Scrubs/Shrub 6 Perennial Ice/Snow 7 Grassland/Herbaceous 8 Agriculture 9 Wetlands Landcover_Level2_NLCD2011. This layer was extracted from the map package NLCD2011_Level2.mpk and contains fifteen classes: Value Class Name 11 Open Water 12 Perennial Ice/Snow 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands Road Density - Road_Density. This raster layer depicts road density (km/km2) calculated for all roads in and around the study area (30 km buffer around BICA) as of 2005. This layer was extracted from the map package AllRoads_rdd.mpk. The map packages mentioned above can be found in the DataStore reference: Bighorn Canyon National Recreation Area Landscape Context, Map Packages. National Park Service. https://irma.nps.gov/DataStore/Reference/Profile/2306146>https://irma.nps.gov/DataStore/Reference/Profile/2306146

Sampling
Samples were collected on board the RSV Aurora Australis between 22 January and 17 February 2016. The cruise surveyed the region south of the Kerguelen Plateau including the Princess Elizabeth Trough and BANZARE Bank in a series of eight transects covering 8165 km. Plankton communities were collected at 45 conductivity temperature depth (CTD) stations and seven additional underway stations, with biological replicates collected at two stations (52 independent sites). Surface water was sampled from 4 plus or minus 2 m depth using the uncontaminated seawater line. Deep Chlorophyll Maximum (DCM, 10-74 m) water samples were obtained using 10 L Niskin bottles mounted on a Seabird 911+ CTD. Plankton communities were size-fractionated by sequentially filtering 10 L seawater through 25 mm 20 micron (nylon) and 5 micron filters (PVDF), and 0.45 micron Sterivex filters (PVDF). Filters were stored frozen at -80 °C.

DNA extraction and high-throughput sequencing
DNA was extracted from half of each filter using the MoBio PowerSoil DNA Isolation kit at the Australian Genome Research Facility (AGRF, Adelaide, Australia; http://www.agrf.org.au). The V4 region of the 18S rDNA (approximately 380 bp excluding primers) was PCR-amplified using universal eukaryotic primers from all extracts and sequenced on an Illumina MiSeq v2 (2 x 250 bp paired-end) following the Ocean Sampling Day protocol (Piredda et al. 2017). Amplicon library preparation and high-throughput sequencing were carried out at the Ramaciotti Centre for Genomics (Sydney, Australia).

Sequence analysis, OTU picking and assignment followed the Biomes of Australian Soil Environments (BASE) workflow (Bissett et al. 2016). Taxonomy was assigned to OTUs based on the PR2 database using the ‘classify.seqs’ command in mothur version 1.31.2 with default settings and a bootstrap cut-off of 60%. OTUs representing any terrestrial contaminants (e.g. human) and samples with low sequencing coverage (less than 7000 reads) were removed from the dataset.

The date of sea ice melt for each station was estimated from daily SSM/I-derived sea-ice spatial concentration from the National Snow and Ice Data Centre (NSIDC) at 25 x 25 km resolution. Days since melt was considered to be the number of days between the date on which sea ice concentration first fell below 15% and the date of sampling.

Other environmental variables included are in situ chlorophyll a, as an indicator of biological production, and near-surface salinity (mean over the upper 10 m) as an indicator for recent sea ice melt. Both environmental measurements were taken from the associated CTD seawater samples. The surface chlorophyll a in seawater (1-2 L) collected in Niskin bottles was analysed by high performance liquid chromatography (HPLC, provided by Karen Westwood and Imojen Pearce, Australian Antarctic Division, doi:10.4225/15/5a94c701b98a8).

Sampling times are given in UTC.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Automated data curation for niche scientific topics, where data quality and contextual accuracy are paramount, poses significant challenges. Bidirectional contextual models such as BERT and ELMo excel in contextual understanding and determinism. However, they are constrained by their narrower training corpora and inability to synthesize information across fragmented or sparse contexts. Conversely, autoregressive generative models like GPT can synthesize dispersed information by leveraging broader contextual knowledge and yet often generate plausible but incorrect (“hallucinated”) information. To address these complementary limitations, we propose an ensemble approach that combines the deterministic precision of BERT/ELMo with the contextual depth of GPT. We have developed a hierarchical knowledge extraction framework to identify perovskites and their associated solvents in perovskite synthesis, progressing from broad topics to narrower details using two complementary methods. The first method leverages deterministic models like BERT/ELMo for precise entity extraction, while the second employs GPT for broader contextual synthesis and generalization. Outputs from both methods are validated through structure-matching and entity normalization, ensuring consistency and traceability. In the absence of benchmark data sets for this domain, we hold out a subset of papers for manual verification to serve as a reference set for tuning the rules for entity normalization. This enables quantitative evaluation of model precision, recall, and structural adherence while also providing a grounded estimate of model confidence. By intersecting the outputs from both methods, we generate a list of solvents with maximum confidence, combining precision with contextual depth to ensure accuracy and reliability. This approach increases precision at the expense of recalla trade-off we accept given that, in high-trust scientific applications, minimizing hallucinations is often more critical than achieving full coverage, especially when downstream reliability is paramount. As a case study, the curated data set is used to predict the endocrine-disrupting (ED) potential of solvents with a pretrained deep learning model. Recognizing that machine learning models may not be trained on niche data sets such as perovskite-related solvents, we have quantified epistemic uncertainty using Shannon entropy. This measure evaluates the confidence of the ML model predictions, independent of uncertainties in the NLP-based data curation process, and identifies high-risk solvents requiring further validation. Additionally, the manual verification pipeline addresses ethical considerations around trust, structure, and transparency in AI-curated data sets.