Analysis of the coordinate files is done by running the matlab-script "Analyse_flights.m". It will allow the user to specify the files to analyse and will ask for the files (calibration file, frames to analyse and reference plate position) it needs in order to run analysis. All required files are included in the folders with the coordinates. The reference plate positions (called Plate points SX) contain three points on the calibration plate in horizontal orientation in the first frame and this will define the coordinate system to put it into real world.

The Matlab file "Analyse_WT_distance.m" will run the calculations of the wing-tip distances using the same coordinates files as with the speed analysis.

"Plot_all_speeds_over_time.m" will calculate accelerations and plot all together.

See "Read Me.txt" for more details.

Dozens of missing epochs in the monthly gravity product of the satellite mission Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission greatly inhibit the complete analysis and full utilization of the data. Despite previous attempts to handle this problem, a general all-purpose gap-filling solution is still lacking. Here we propose a non-parametric, data-adaptive and easy-to-implement approach - composed of the Singular Spectrum Analysis (SSA) gap-filling technique, cross-validation, and spectral testing for significant components - to produce reasonable gap-filling results in the form of spherical harmonic coefficients (SHCs). We demonstrate that this approach is adept at inferring missing data from long-term and oscillatory changes extracted from available observations. A comparison in the spectral domain reveals that the gap-filling result resembles the product of GRACE missions below spherical harmonic degree 30 very well. As the degree increases above 30, the amplitude per degree of the gap-filling result decreases more rapidly than that of GRACE/GRACE-FO SHCs, showing effective suppression of noise. As a result, our approach can reduce noise in the oceans without sacrificing resolutions on land. The gap filling dataset is stored in the “SSA_filing/" folder. Each file represents a monthly result in the form of spherical harmonics. The data format follows the convention of the site ftp://isdcftp.gfz-potsdam.de/grace/. Low degree corrections (degree-1, C20, C30) have been made. The code to generate the dataset is located in the “code_share/“ folder, with an example for C30. The model-based Greenland mass balance result for data validation (results given in the paper) is provided in the "Greenland_SMB-D.txt” file.

The Measurement Infrastructure Gap Analysis in Utah’s Great Salt Lake Basin was a comprehensive inventory and analysis of existing diversion and stream measurement infrastructure along 19 primary river systems, as well as a preliminary investigation of measurement infrastructure gaps around Great Salt Lake proper. The purpose of this “Gap Analysis” was to develop methods to identify and prioritize areas throughout the Great Salt Lake basin where new or updated measurement infrastructure is needed to serve diverse objectives. The following gaps were identified: (1) existing measurement infrastructure quality and completeness gaps, (2) stakeholder identified gaps, and (3) potential spatial gaps in hydrologic understanding. By adapting the prioritization schema originally presented in the Colorado River Metering and Gaging and Gap Analysis to equally weight these three gap types at the HUC12 scale, a flexible framework for prioritizing new or updated measurement infrastructure in areas with large cumulative measurement gaps was developed, and high, medium, and low priority HUC12s were identified.

Results showed that 250 diversion and 28 stream measurement devices along primary systems had at least one quality and/or completeness gap. The most common quality and completeness gaps were insufficient device types, lack of telemetry, and data record interval. Stakeholders suggested 50 instances of new or updated diversion measurement infrastructure, 95 instances of new or updated stream measurement infrastructure, and 39 recommendations for continued funding of existing measurement infrastructure—totaling 185 stakeholder-identified gaps. To provide a spatially consistent approach to identifying potential gaps in hydrologic understanding, geospatial datasets describing features or attributes that can impact local hydrology were used to identify measurement gaps at the HUC12 scale. Among HUC12s that overlapped with the river systems included in this analysis, HUC12s with the greatest number of potential spatial gaps were in the Bear River sub-basin and near reservoirs in the Weber River sub-basin.

Based on the prioritization schema applied to synthesize these three gap types, there were 52 HUC12s along primary systems classified as high priority for measurement improvement. Of the 250 existing diversion and 28 stream measurement devices with at least one quality and/or completeness gap, 217 and 10 devices, respectively, were located within high priority HUC12s. Most stakeholder-identified gaps identified in the Weber and Jordan River sub-basins also fell within high-priority HUCs. Eighteen unique agencies suggested new or updated measurement infrastructure or continued funding of existing measurement infrastructure in high-priority HUC12s, demonstrating some consensus regarding measurement gaps in critical areas. There were 6 high priority HUC12s with no existing measurement infrastructure quality and completeness gaps, and 11 high priority HUC12s with no stakeholder-identified gaps. High priority HUC12s highlighted only due to potential spatial gaps may warrant additional investigation to further understand potential measurement gaps in these HUC12s.

Because the prioritization schema applied equally weighted all three gap types, it likely does not fully represent the diverse missions and priorities of different stakeholder groups. To facilitate an adaptable approach to prioritize measurement gaps within the Great Salt Lake basin, raw data for each of the three gap types are provided to allow varied prioritization schemes to be developed by weighting gap types differently or considering subsets of data. These data provide the basis for stakeholders within the Great Salt Lake basin to collectively prioritize future investments in gaging infrastructure and better manage water throughout the Great Salt Lake basin.

The dataset is provided in a single .xlsx file named "eucalyptus_growth_environment_data_V2.xlsx" and consists of fifteen sheets:

Codebook: This sheet details the index, values, and descriptions for each field within the dataset, providing a comprehensive guide to understanding the data structure.

ALL NODES: Contains measurements from all devices, totalling 102,916 data points. This sheet aggregates the data across all nodes.

GWD1 to GWD10: These subset sheets include measurements from individual nodes, labelled according to the abbreviation “Generic Wireless Dendrometer” followed by device IDs 1 through 10. Each sheet corresponds to a specific node, representing measurements from ten trees (or nodes).

Metadata: Provides detailed metadata for each node, including species, initial diameter, location, measurement frequency, battery specifications, and irrigation status. This information is essential for identifying and differentiating the nodes and their specific attributes.

Missing Data Intervals: Details gaps in the data stream, including start and end dates and times when data was not uploaded. It includes information on the total duration of each missing interval and the number of missing data points.

Missing Intervals Distribution: Offers a summary of missing data intervals and their distribution, providing insight into data gaps and reasons for missing data.

All nodes utilize LoRaWAN for data transmission. Please note that intermittent data gaps may occur due to connectivity issues between the gateway and the nodes, as well as maintenance activities or experimental procedures.

Software considerations: The provided R code named “Simple_Dendro_Imputation_and_Analysis.R” is a comprehensive analysis workflow that processes and analyses Eucalyptus growth and environmental data from the "eucalyptus_growth_environment_data_V2.xlsx" dataset. The script begins by loading necessary libraries, setting the working directory, and reading the data from the specified Excel sheet. It then combines date and time information into a unified DateTime format and performs data type conversions for relevant columns. The analysis focuses on a specified device, allowing for the selection of neighbouring devices for imputation of missing data. A loop checks for gaps in the time series and fills in missing intervals based on a defined threshold, followed by a function that imputes missing values using the average from nearby devices. Outliers are identified and managed through linear interpolation. The code further calculates vapor pressure metrics and applies temperature corrections to the dendrometer data. Finally, it saves the cleaned and processed data into a new Excel file while conducting dendrometer analysis using the dendRoAnalyst package, which includes visualizations and calculations of daily growth metrics and correlations with environmental factors such as vapour pressure deficit (VPD).

This collection presents a gap-filled, gridded time series dataset of daily ocean temperature and current, collected from an array of 6 coastal Integrated Marine Observing System (IMOS) moorings off the southwest coast of Western Australia (WA) during 2009-2023, at depths ranging from 47 m to 500 m. Self-Organizing Map (SOM) is used to fill the data gaps.

The collection also provides a daily gridded mooring dataset of temperature, salinity, and current without gap-filling. Monthly average data are also included. Monthly data were then derived from daily data if there were more than 10 days of data during that month.

This integrated dataset provides an overview of data availability and allows users to have quick access to the mooring data, without the need of manipulating over one thousand files individually. This unique dataset offers an invaluable baseline perspective on water column properties and temporal variability in WA coastal waters. The data can be used to characterise subsurface features of extreme events such as marine heatwaves, marine cold-spells, and to detect long-term change signals along the WA coast, influenced by the Leeuwin Current and the wind-driven Capes Current.

Lineage: This collection includes two data products: the unfilled gridding data and the in-filled gridding data. For the first product, initially raw data (FV00) were processed using IMOS Matlab Toolbox, then Quality Assurance (QA) and Quality Control (QC) of the data were performed using the Toolbox and assessed by oceanographers (https://doi.org/10.25919/9gb1-ne81). After that, quality-controlled data (FV01) were concatenated, and then (linearly) interpolated to a grid of 1m vertical resolution and averaged daily. Monthly data were then derived from daily data if there were more than 10 days of data during that month. For the second product, based on the unfilled data, we firstly had extrapolated temperature and current vertical profiles, and then selected these profiles for training Self-Organizing Map (SOM), thereby improving the accuracy of the input data's topological structure. Daily data vectors containing missing values were mapped onto SOM grids using the best matching unit determined by a similarity function, and the missing data points were filled by replacing them with the corresponding SOM unit.

IntroductionThe COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy.MethodsA qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method.ResultsNon-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity.DiscussionWhile the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

The paper offers a hitherto-lacking comprehensive appraisal of solidaristic transfers by European Union Member States (EUMS) during the first year of the COVID-19 pandemic. These transfers include bilateral assistance, collective burden-sharing on the EU level, and even external EU aid. The article uses data on inter-EUMS solidarity actions collected by European Solidarity Tracker (EST), a widely referenced dataset on pandemic-related actions of solidarity. It cleans this data to address its deficiencies, including by filtering out symbolic and tokenistic actions, to focus on instances of truly meaningful assistance between EUMS. The EST is complemented by two further sets of data: an overview of EU-level measures, as examples of institutionalized and institutionally enabled forms of solidarity; and, given the global connectedness of the EU, data on pandemic assistance to developing countries. Based on this broad understanding of solidaristic transfers, the EU’s response is found to have been significant but insufficient overall to fill the gaps in pandemic response. The gaps identified have inevitably fed into the pandemic, contributing to permissive conditions for its resurgence. EU-level measures mattered, but practical manifestations of bilateral solidarity between EUMS have been haphazard. Furthermore, while the EU increased its external health and other development aid considerably during 2020, this by no means made for a well-allocated or adequately resourced pandemic response globally.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

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   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

Climate change is affecting the biodiversity, ecosystem services and the well-being of people that live in the Arctic tundra. Understanding the societal implications and adapting to these changes depend on knowledge produced by multiple disciplines. We analysed peer-reviewed publications to identify the main research themes relating to the Arctic tundra and assessed to what extent current research build on multiple disciplines to confront the upcoming challenges of rapid environmental changes. We used a topic- modelling approach, based on the Latent Dirichlet Allocation algorithm to detect topics based on semantic similarity. We found that plant and soil ecology dominate the tundra research and are highly connected to other ecological disciplines and biophysical sciences. Despite the fivefold increase in the number of publications during the past decades, the proportion of studies that address societal implications of climate change remains low. The strong scientific interest in the tundra reflects the concern of the rapid warming of the Arctic, but few studies include the cross-disciplinary approach necessary to fully assess the implications

Automatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled. Asa Research Station (2025). Meteorological data from Feresjön, floating platform, 2017-08-18–2021-10-27 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/6FDd9r61IUlHOTfmJGAIGO3Y

The global data visualization market, valued at $9.84 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 10.95% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across various industries necessitates effective visualization tools for insightful analysis and decision-making. Furthermore, the rising adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, driving market growth. Advances in artificial intelligence (AI) and machine learning (ML) are integrating seamlessly with data visualization platforms, enhancing automation and predictive capabilities, further stimulating market demand. The BFSI (Banking, Financial Services, and Insurance) sector, along with IT and Telecommunications, are major adopters, leveraging data visualization for risk management, fraud detection, customer relationship management, and network optimization. However, challenges remain, including the need for skilled professionals to effectively utilize these tools and concerns regarding data security and privacy. The market segmentation reveals a strong presence of executive management and marketing departments across organizations, highlighting the strategic importance of data visualization in business operations. The market's competitive landscape is characterized by established players like SAS Institute, IBM, Microsoft, and Salesforce (Tableau), along with emerging innovative companies. This competition fosters innovation and drives down costs, making data visualization solutions more accessible to a broader range of businesses and organizations. Regional variations in market penetration are expected, with North America and Europe currently holding significant shares, but Asia Pacific is poised for substantial growth, driven by rapid digitalization and technological advancements in the region. The on-premise deployment mode still holds a considerable market share, though the cloud/on-demand segment is experiencing faster growth due to its inherent advantages. The ongoing trend towards self-service business intelligence (BI) tools is empowering end-users to access and analyze data independently, increasing the overall market demand for user-friendly and intuitive data visualization platforms. Future growth will depend on continued technological advancements, expanding applications across diverse industries, and addressing the existing challenges related to data skills gaps and security concerns. This report provides a comprehensive analysis of the Data Visualization Market, projecting robust growth from $XX Billion in 2025 to $YY Billion by 2033. It covers the period from 2019 to 2033, with a focus on the forecast period 2025-2033 and a base year of 2025. This in-depth study examines key market segments, competitive landscapes, and emerging trends influencing this rapidly evolving industry. The report is designed for executives, investors, and market analysts seeking actionable insights into the future of data visualization. Recent developments include: September 2022: KPI 360, an AI-driven solution that uses real-time data monitoring and prediction to assist manufacturing organizations in seeing various operational data sources through a single, comprehensive industrial intelligence dashboard that sets up in hours, was recently unveiled by SymphonyAI Industrial., January 2022: The most recent version of the IVAAP platform for ubiquitous subsurface visualization and analytics applications was released by INT, a top supplier of data visualization software. IVAAP allows exploring, visualizing, and computing energy data by providing full OSDU Data Platform compatibility. With the new edition, IVAAP's map-based search, data discovery, and data selection are expanded to include 3D seismic volume intersection, 2D seismic overlays, reservoir, and base map widgets for cloud-based visualization of all forms of energy data.. Key drivers for this market are: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Potential restraints include: Lack of Tech Savvy and Skilled Workforce/Inability. Notable trends are: Retail Segment to Witness Significant Growth.

This collection of 9-second raster data was compiled for use in modelling biodiversity pattern by developers engaged in supporting the New South Wales Biodiversity Indicators Program. Substrate and landform data derive from existing collections and have been altered from their native format to fill missing and erroneous data gaps as described in the lineage. Climate data were derived using existing methods as described in the lineage. Masks derived or adopted for use in processing the data are included in this collection. Data are supplied in ESRI float grid format, GCS GDA94 Geographic Coordinate System Geocentric Datum of Australia (GDA) 1994.
Lineage: The abiotic environmental data in this collection are grouped by broad type - climate, substrate and landform. Datasets are provided in separate compressed folders (*.zip or *.7z). An excel spreadsheet is included with the collection that list and briefly describes all datasets and their source URLs, and the processing location of the data in the CSIRO project archive. A lineage document summarises the mask and gap filling processes. Mask data were developed from existing spatial boundary data including Australian coastline, State and administration boundaries, and previous raster modelling masks for the NARCLIM region. The data gap filling process was conducted in three stages (python processing scripts are included in this collection). In the first stage, the process used a 10 cell Inverse Distance Weighted (IDW) algorithm to fill no Data areas with data. The IDW algorithm used the distance of data values in the search radius as inverse weights in a neighbourhood average. To deal with remaining larger gaps, a second stage IDW was run on the outputs of the first stage with an increased radius of 500 cells. Any remaining data gaps were filled with a global data average. This process of data filling may make the data unsuitable for other uses and should be carefully considered before use. Images of each dataset are provided in the collection for ease of reference. Data are supplied in ESRI float grid format, GCS GDA94 Geographic Coordinate System Geocentric Datum of Australia (GDA) 1994.

Automatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled. Svartberget Research Station (2025). Meteorological data from Degerö, 2001–2014 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/_mzV3qJbg4LKLlGbx6_VAIou

DOI WHU-SWPU-GOGR2022S is a static gravity field model complete to spherical harmonic degree and order of 300 by combining GOCE and GRACE normal equations. Details of the processing procedures are as follows: (1) Details of the GOCE processing procedures: (1a) Input data: -- GOCE SGG data: EGG_NOM_2 (GGT: Vxx, Vyy, Vzz and Vxz) in GRF (9/10/2009-20/10/2013) -- GOCE SST data: SST_PKI_2, SST_PCV_2, SST_PRD_2 (9/10/2009-20/10/2013) -- Attitude: EGG_NOM_2 (IAQ), SST_PRM_2 (PRM) -- Non-conservative force: Common mode ACC (GG_CCD_1i) -- Background model: tidal model (solid etc.), third-body acceleration, relativistic corrections, ... (1b) Data progress strategies: -- Data preprocessing - Gross outlier elimination and interpolation (only for the data gaps less than 40s). - Splitting data into subsections for gaps > 40s -- The normal equation from SST data - Point-wise acceleration approach (PAA) - Extended Differentiation Filter (low-pass) - Max degree: up to 130 - Data: PKI, PCV, CCD -- The normal equation from SGG data - Direct LS method - Max degree: up to 300 - Data: GGT, PRD, IAQ, PRM - Band-pass filter: used to deal with colored-noise of GGT observations (pass band 0.005-0.100Hz ) - Forming the normal equations according to subsections - Spherical harmonic base function transformation instead of transforming GGT from GRF to LNRF -- Combination of SGG and SST - Max degree: up to 300 - The VCE technique is used to estimate the relative weights for Vxx, Vyy, Vzz and Vxz - Tikhonov Regularization Technique (TRT) is only applied to near (zonal) terms (m<20, n200) - Strictly inverse the normal matrix based on OpenMP (2) Details of the GRACE processing procedures: (2a) Input data: -- GRACE L1B (JPL) data products: GNV1B RL02, ACC1B RL02, SCA1B RL03 and KBR1B RL03 -- AOD1B RL06 (GFZ) de-aliasing product -- Data period: 04/2002-05/2017 (2b) Data preprocessing: -- Splitting data of SCA1B into subsections for gaps > 120s and interpolation with polynomial for gaps 5s and interpolation with polynomial for gaps <= 5s -- Gross outlier elimination ACC1B with a moving window of length 10 min, and interpolation with polynomial -- Pre-calibration of ACC1B with a-priori bias and scale Parameters provided by GRACE TN-02 (2c) Calculation method: - dynamic approach - numerical integrator: 8th-order Gauss-Jackson integrator - integrator step: 5 seconds - arc length: 24 hours (2d) Combination - GNV1B and KBR1B are combined with their a-priori precision, i.e. 2cm of GNV1B and 2um/s of KBR1B - The normal equations of different months are combined with variance components estimation (2e) Force models: - Earth's static gravity field: GGM05s up to d/o 180 - Solid earth tides: IERS 2010 - Ocean tides: FES2014b up to d/o 180 - Solid Earth pole tide: IERS 2010 - Ocean pole tide: Desai 2002 up to d/o 180 - N-body Perturbation: the Sun and Moon with JPL DE421 - atmospheric tides: Bode and Biancale model - AOD1B product: AOD1B RL06 model up to d/o 180 - General Relativistic effects: Schwarzschild terms of IERS 2010

The Great Lakes Environmental Database (GLENDA) houses environmental data collected by EPA Great Lakes National Program Office (GLNPO) programs that sample water, aquatic life, sediments, and air to assess the health of the Great Lakes ecosystem. GLENDA is available to the public on the EPA Central Data Exchange (CDX). A CDX account is required, which anyone may create. GLENDA offers “Ready to Download Data Files” prepared by GLNPO or a “Query Data” interface that allows users to select from predefined parameters to create a customized query. Query results can be downloaded in .csv format. GLNPO programs providing data in GLENDA include the Great Lakes Water Quality Survey and Great Lakes Biology Monitoring Program (1983-present, biannual monitoring throughout the Great Lakes to assess water quality, chemical, nutrient, and physical parameters, and biota such as plankton and benthic invertebrates), the Great Lakes Fish Monitoring and Surveillance Program (1977-present, annual analysis of top predator fish composites to assess historic and emerging persistent, bioaccumulative, or toxic chemical contaminants), the Cooperative Science and Monitoring Initiative (2002-present, intensive water quality and biology sampling of one lake per year focusing on key challenges and data gaps), the Great Lakes Integrated Atmospheric Deposition Network (1990-present, monitoring Great Lakes air and precipitation for persistent toxic chemicals), the Lake Michigan Mass Balance Study (1993-1996, analyzed the atmosphere, tributaries, sediments, water column, and biota of Lake Michigan for nutrients, atrazine, PCBs, trans-nonachlor, and mercury modelling), and the Great Lakes Legacy Act (1996-present, evaluations of sediment contamination in Areas of Concern). GLENDA is updated frequently with new data.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO3ANCQA Version 1 is a Level 3 (L3) product that provides Quality Assessment (QA) fields for all ancillary data used in L3 and Level 4 (L4) products generated by Jet Propulsion Laboratory (JPL). No quality flags are generated for the L3 or L4 products. Instead, the quality flags of the source data products are resampled by nearest neighbor onto the geolocation of the ECOSTRESS scene. A quality flag array for each input dataset, when available, is collected into the combined QA product.The ECO3ANCQA Version 1 data product contains variables of quality flags for ECOSTRESS cloud mask, Landsat 8, land cover type, albedo, MODIS Terra aerosol, MODIS Terra Cloud 1 km, MODIS Terra Cloud 5 km, MODIS Terra atmospheric profile, vegetation indices, MODIS Terra gross primary productivity, and MODIS water mask.Known Issues Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.

Automatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled. Röbäcksdalen Field Research Station (2025). Meteorological data from Röbäcksdalen Research Area, AWS, 2019-01-01–2019-12-31 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/Jn2Za8Hs7iXsjag-i9fOrQYU

Automatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled. Svartberget Research Station (2025). Meteorological data from Svartberget, Hygget AWS, 2019–2024 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/ND-ESQJku3tDi9GHvC7i_mSH

Abstract1.Increasing demand for benefits provided by riverine ecosystems threatens their sustainable provision. The ecosystem service concept is a promising avenue to inform riverine ecosystem management, but several challenges have prevented the application of this concept. 2.We quantitatively assess the field of riverine ecosystem services’ progress in meeting these challenges. We highlight conceptual and methodological gaps, which have impeded integration of the ecosystem service concept into management. 3.Across 89 relevant studies, 33 unique riverine ecosystem services were evaluated, for a total of 404 ecosystem service quantifications. Studies quantified between one and 23 ecosystem services, although the majority (55%) evaluated three or less. Among studies that quantified more than one service, 58% assessed interactions between services. Most studies (71%) did not include stakeholders in their quantification protocols, and 34% developed future scenarios of ecosystem service provision. Almost half (45%) conducted monetary valuation, using 16 methods. Only 9% did not quantify or discuss uncertainties associated with service quantification. The indicators and methods used to quantify the same type of ecosystem service varied. Only 3% of services used indicators of capacity, flow, and demand in concert. 4.Our results suggest indicators, data sources, and methods for quantifying riverine ecosystem services should be more clearly defined and accurately represent the service they intend to quantify. Furthermore, more assessments of multiple services across diverse spatial extents and of riverine service interactions are needed, with better inclusion of stakeholders. Addressing these challenges will help riverine ecosystem service science inform river management. 5.Synthesis and applications. The ecosystem service concept has great potential to inform riverine ecosystem management and decision making processes. However, this review of riverine ecosystem service quantification uncovers several remaining research gaps, impeding effective use of this tool to manage riverine ecosystems. We highlight these gaps and point to studies showcasing methods that can be used to address them. Usage notesReview of riverine ecosystem service quantification studiesThis file contains a database of studies that quantified riverine ecosystem services prior to April 2016, as well as quantitative data on the location of each study, the types and numbers of ecosystem services evaluated, and the methods used to quantify services.Hanna_Riverine ES Review Database.xlsx

This survey aimed to help us gain an understanding of research data stewardship activities in UCC, the scope of those activities, identify any gaps in current resources and skills and work out where the Research Data Service fits with related roles and services. We hoped this activity would also help with the development of a data stewardship network across UCC for support, skills sharing, peer learning and the development of tailored skills development programs within UCC. It would also provide an evidence base to inform the model UCC should adopt in meeting its future research data requirements.

Funders and publishers increasingly require researchers to formally manage their data and encourage or mandate FAIR and/or Open Data outputs. Both the National and European Codes of Research Conduct recognise that data management is central to research integrity and the quality and trustworthiness of research outputs across all disciplines. Research infrastructures in Europe are currently in a phase of development with continued expansion of the European Open Science Cloud (EOSC) and related
services. Successive reports internationally (Realising the EOSC, 2016, Turning FAIR into a Reality, 2018) and our own recently compiled National Landscape Report (NORF, 2021) highlighted a resource and skills gap in meeting the expectations and potential of FAIR research data and related research
infrastructures. Specifically, in relation to FAIR and Open Data, a set of skills, competencies, and responsibilities have been identified and grouped together under the umbrella of a new “Research Data Steward” role. Research data stewardship encompasses all the various tasks and responsibilities that
relate to research data management throughout the entire research lifecycle. The role of data steward is not universally defined yet and is influenced by the context and the needs of the researcher or unit. Across Europe, Research Performing Organisations have taken concrete steps to address this gap, for example by appointing new data steward positions or by re-focusing existing institutional skills and supports into designated competency centres for research data supports. TU Delft is an exemplar where eight newly established embedded data stewards, with domain expertise in the relevant faculty, complement a similar number of support staff based in central services such as the Library and IT Services.

In UCC the Research Data Service provides a range of data stewardship supports to the research community from advisory to tailored training. The Research Data Service and Research Data Coordinator work closely with related services and roles to provide holistic advice on research data management to the UCC research community. The Clinical Research Facility–Cork has also developed a data stewardship service which is available on a consultancy basis to funded human focused research projects. However, the ask of researchers in terms of funder mandated data management plans and commitments to FAIR and Open Data continues to increase. Certainly in the case of the Research Data Service full capacity is fast approaching. As funders embed Open Science, and by extension data management, FAIR, and Open Data more firmly in their policies and requirements there is a risk that this will impact the competitiveness of our funding applications and the reach, impact and quality of our research outputs if we cannot meet researchers increasingly complex needs for research data stewardship support.

We know that there are those engaged in research data stewardship activities throughout UCC although this may not be reflected in their job title. Those who engage in research data stewardship activities do not always identify as Data Stewards but contribute significantly to the data management lifecycle associated with research projects. Each stage of a research project can have specialist data stewardship requirements - these tasks are performed by people in a range of roles and positions including researchers, project managers, data managers, statisticians and data analysts, research assistants, technicians, systems administrators, or research software engineers to name but a few. To develop a holistic and coordinated approach data stewardship and research data management we needed to hear from the whole research ecosystem, those engaged in research and those facilitating it.