Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 co...

Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.

This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements.

The Intelligent Semantic Data Service market is experiencing robust growth, driven by the increasing need for organizations to extract actionable insights from rapidly expanding data volumes. The market's complexity necessitates sophisticated solutions that go beyond traditional data analytics, focusing on understanding the meaning and context of data. This demand is fueled by advancements in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), which power semantic analysis engines. Key players like Google, IBM, Microsoft, Amazon, and others are heavily investing in this space, developing and deploying powerful solutions that cater to various industries, from finance and healthcare to retail and manufacturing. The market's projected Compound Annual Growth Rate (CAGR) suggests a significant expansion over the forecast period (2025-2033). We estimate the 2025 market size to be approximately $15 billion, based on industry reports and observed growth trajectories in related AI segments. This figure is expected to reach approximately $35 billion by 2033. Several factors contribute to this growth, including the rising adoption of cloud-based solutions, the need for improved data governance, and a growing emphasis on data-driven decision-making. However, the market also faces certain restraints. High implementation costs, the need for specialized expertise, and data security concerns can hinder widespread adoption. Furthermore, the market is characterized by a relatively high barrier to entry, favoring established players with significant R&D capabilities. Nevertheless, the potential benefits of unlocking the true value of unstructured data through intelligent semantic analysis are compelling enough to drive continued investment and innovation in this rapidly evolving market. Segmentation within the market is likely based on deployment type (cloud, on-premise), service type (data enrichment, knowledge graph creation, semantic search), and industry vertical. The geographic distribution shows a strong concentration in North America and Europe, followed by a steady growth in the Asia-Pacific region, driven by increasing digitalization efforts.

CTOS Basis team works with our data partners and instituted a well defined methodology to produce comprehensive report with various analysis on credit information, financial, business operation, industries and credit risk.

Contents in the report include: • Latest registration details & capital structure of a company • Details of shareholders, directors and the management team • Litigation check & credit information on a company • Latest financial information • Various financial ratios & financial analysis • Payment records, Clientele, Business operations • Latest news check • Current investigation • Latest economic and industrial data • Industry analysis • Credit risk evaluation & credit rating

Performance measures are data metrics defined and tracked by city departments to measure the city government’s effectiveness and efficiency of service delivery. Data for the performance measures are derived from the biennial Resident Survey as well as department data tracking systems. Each performance measure is connected to one of the strategic goals and objectives that the City has defined as a high priority. The performance measures will be reviewed and refined annually to ensure they are representative of the priorities set out by City Council and the community. The performance measures in this filtered view is for the "Learning and Enrichment Opportunities" strategic goal and objective.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Digitalizing highway infrastructure is gaining interest in Germany and other countries due to the need for greater efficiency and sustainability. The maintenance of the built infrastructure accounts for nearly 30% of greenhouse gas emissions in Germany. To address this, Digital Twins are emerging as tools to optimize road systems. A Digital Twin of a built asset relies on a geometric-semantic as-is model of the area of interest, where an essential step for automated model generation is the semantic segmentation of reality capture data. While most approaches handle data without considering real-world context, our approach leverages existing geospatial data to enrich the data foundation through an adaptive feature extraction workflow. This workflow is adaptable to various model architectures, from deep learning methods like PointNet++ and PointNeXt to traditional machine learning models such as Random Forest. Our four-step workflow significantly boosts performance, improving overall accuracy by 20% and unweighted mean Intersection over Union (mIoU) by up to 43.47%. The target application is the semantic segmentation of point clouds in road environments. Additionally, the proposed modular workflow can be easily customized to fit diverse data sources and enhance semantic segmentation performance in a model-agnostic way.

Raw sequencing data from Denitrifying Anaerobic Methane Oxidation (DAMO) experiments and the relevant statistical data generated by various bioinformatics tools. This dataset is not publicly accessible because: All the experiments for this study were not performed in EPA but in co-authors' institution which has managed the project and prepared a manuscript for peer-reviewed journal submission. It can be accessed through the following means: The raw data will be made available by the authors on request (Dr. Yaohuan Gao, gaoyaohuan@xjtu.edu.cn). Format: Not available because the raw data was not generated in EPA. This dataset is associated with the following publication: Xia, L., Y. Wang, P. Yao, H. Ryu, Z. Dong, C. Tan, S. Deng, H. Liao, and Y. Gao. The effects of model insoluble copper compounds in anoxic sedimentary environment on denitrifying anaerobic methane oxidation (DAMO) activity. Microorganisms. MDPI, Basel, SWITZERLAND, 12(11): 2259, (2024).

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

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

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

CTOS Basis team works with our data partners and instituted a well defined methodology to produce comprehensive report with various analysis on credit information, financial, business operation, industries and credit risk.

Contents in the report include: • Latest registration details & capital structure of a company • Details of shareholders, directors and the management team • Litigation check & credit information on a company • Latest financial information • Various financial ratios & financial analysis • Payment records, Clientele, Business operations • Latest news check • Current investigation • Latest economic and industrial data • Industry analysis • Credit risk evaluation & credit rating

The size of the Data as a Service market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 20.00% during the forecast period.Data as a Service, in its most simplistic form, provides an on-demand cloud-based service model for data and analytics. The model will help business use the power of data by not requiring large upfront investments in data storage, processing, and analysis infrastructure. Therefore, data and insights as a service will make DaaS simple to manage, reduce operational costs, and accelerate time-to-value.DaaS suppliers deliver a collection of data services which may include data integration, data cleansing, data enrichment, and data analytics. These services ensure businesses are able to access, and thereby use, hundreds and thousands of data sources located internally or externally for valuable insight and informed decisions. Primarily, DaaS can help out those organizations lacking internal resources and expertise or in their means to gather, handle, and process significant data. Business results are therefore better outsourced with DaaS because they can, at a given time, tend to more core competencies related to the business. Recent developments include: September 2022: Asigra Inc., an ultra-secure backup and recovery pioneer, declared the general availability of Tigris Data Protection software with Content Disarm & Reconstruction (CDR). The addition of CDR makes Asigra the most security-forward backup and recovery software platform available, adding to its extensive suite of security features., June 2022: IMAT Solutions, a real-time healthcare data management and population health reporting solutions provider, announced the launch of a new Data-as-a-Service (DaaS) offering for health payers. The new DaaS solution meets the new Centers for Medicare & Medicaid Services (CMS) effort to transition all quality measures used in its reporting programs to digital quality measures (dQMs).. Key drivers for this market are: Growing Penetration of Data-based Decisions Among Enterprises, Transformation of Enterprises Leading to Real-time Analytics Demand. Potential restraints include: Concerns Regarding Privacy and Security. Notable trends are: BFSI Sector to Witness High Growth.

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

DESeq2 differential abundance output for genes with q < 0.05 and |log₂ fold change| > 1 in cancer vs control plasma samples:

• differentialabundance_pancancer.txt: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)
• differentialabundance_threecancer.txt: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls)
  • Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.

Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):

• customgenesets.txt: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data.
  • Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set
• GSEA_pancancer.txt & GSEA_threecancer.txt: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively
  • Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in customgenesets.txt); pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls

Animal welfare requires the adequate housing of animals to ensure health and well-being. The application of environmental enrichment is a way to improve the well-being of laboratory animals. However, it is important to know whether these enrichment items can be incorporated in experimental mouse husbandry without creating a divide between past and future experimental results. Previous small-scale studies have been inconsistent throughout the literature, and it is not yet completely understood whether and how enrichment might endanger comparability of results of scientific experiments. Here, we measured the effect on means and variability of 164 physiological parameters in 3 conditions: with nesting material with or without a shelter, comparing these 2 conditions to a “barren” regime without any enrichments. We studied a total of 360 mice from each of 2 mouse strains (C57BL/6NTac and DBA/2NCrl) and both sexes for each of the 3 conditions. Our study indicates that enrichment affects the mean values of some of the 164 parameters with no consistent effects on variability. However, the influence of enrichment appears negligible compared to the effects of other influencing factors. Therefore, nesting material and shelters may be used to improve animal welfare without impairment of experimental outcome or loss of comparability to previous data collected under barren housing conditions.

All methods are fully described in the material and methods section of the manuscript.

Establishment of a syndication flow from the department of Vendée (85) with the data from the “Restoration” schedule with enriched data: name, address, type of restaurant, means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, payment methods accepted, opening dates, rates, details of visits, videos.

Data Quality Management Software Market Outlook

The global data quality management software market size was valued at approximately USD 1.5 billion in 2023 and is anticipated to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.8% during the forecast period. This growth is largely driven by the increasing complexity and exponential growth of data generated across various industries, necessitating robust data management solutions to ensure the accuracy, consistency, and reliability of data. As organizations strive to leverage data-driven decision-making and optimize their operations, the demand for efficient data quality management software solutions continues to rise, underscoring their significance in the current digital landscape.

One of the primary growth factors for the data quality management software market is the rapid digital transformation across industries. With businesses increasingly relying on digital tools and platforms, the volume of data generated and collected has surged exponentially. This data, if managed effectively, can unlock valuable insights and drive strategic business decisions. However, poor data quality can lead to erroneous conclusions and suboptimal performance. As a result, enterprises are investing heavily in data quality management solutions to ensure data integrity and enhance decision-making processes. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in data quality management software is further propelling the market, offering automated data cleansing, enrichment, and validation capabilities that significantly improve data accuracy and utility.

Another significant driver of market growth is the increasing regulatory requirements surrounding data governance and compliance. As data privacy laws become more stringent worldwide, organizations are compelled to adopt comprehensive data quality management practices to ensure adherence to these regulations. The implementation of data protection acts such as GDPR in Europe has heightened the need for data quality management solutions to ensure data accuracy and privacy. Organizations are thus keen to integrate robust data quality measures to safeguard their data assets, maintain customer trust, and avoid hefty regulatory fines. This regulatory-driven push has resulted in heightened awareness and adoption of data quality management solutions across various industry verticals, further contributing to market growth.

The growing emphasis on customer experience and personalization is also fueling the demand for data quality management software. As enterprises strive to deliver personalized and seamless customer experiences, the accuracy and reliability of customer data become paramount. High-quality data enables organizations to gain a 360-degree view of their customers, tailor their offerings, and engage customers more effectively. Companies in sectors such as retail, BFSI, and healthcare are prioritizing data quality initiatives to enhance customer satisfaction, retention, and loyalty. This consumer-centric approach is prompting organizations to invest in data quality management solutions that facilitate comprehensive and accurate customer insights, thereby driving the market's growth trajectory.

Regionally, North America is expected to dominate the data quality management software market, driven by the region's technological advancements and high adoption rate of data management solutions. The presence of leading market players and the increasing demand for data-driven insights to enhance business operations further bolster market growth in this region. Meanwhile, the Asia Pacific region is witnessing substantial growth opportunities, attributed to the rapid digitalization across emerging economies and the growing awareness of data quality's role in business success. The rising adoption of cloud-based solutions and the expanding IT sector are also contributing to the market's regional expansion, with a projected CAGR that surpasses other regions during the forecast period.

Component Analysis

The data quality management software market is segmented by component into software and services, each playing a pivotal role in delivering comprehensive data quality solutions to enterprises. The software component, constituting the core of data quality management, encompasses a wide array of tools designed to facilitate data cleansing, validation, enrichment, and integration. These software solutions are increasingly equipped with advanced features such as AI and ML algorithms, enabling automated data quality processes that si

QURNAS is a tool to quantify RNA splicing events from RNA-seq data. It will generate a file with all splicing events detected in the samples analysed which includes the number of reads per event for each sample. Additionally, it provides an enrichment score per sample and per event, as well as its statistical significance. This greatly facilitates the identification of aberrant RNA splicing events. A description of the event in HGVS nomenclature is also provided.

Ecosystems are connected by flows of nutrients and organisms. Changes to connectivity and nutrient enrichment may destabilise ecosystem dynamics far from the nutrient source. We used gradostats to examine the effects of trophic connectivity (movement of consumers and producers) versus nutrient-only connectivity on the dynamics of Daphnia pulex (consumers) and algae (resources) in two metaecosystem configurations (linear vs. dendritic). We found that Daphnia peak population size and instability (coefficient of variation; CV) increased as distance from the nutrient input increased, but these effects were lower in metaecosystems connected by all trophic levels compared to nutrient-only connected systems and/or in dendritic compared to linear systems. We examined the effects of trophic connectivity (i.e. both trophic levels move rather than one or the other) using a generic model to qualitatively assess whether the expectations align with the ecosystem dynamics we observed. Analysis of our model shows that increased Daphnia population sizes and fluctuations in consumer-resource dynamics are expected with nutrient connectivity, with this pattern being more pronounced in linear rather than dendritic systems. These results confirm that connectivity may propagate and even amplify instability over a metaecosystem to communities distant from the source disturbance, and suggest a direction for future experiments, that recreate conditions closer to those found in natural systems.

Methods Our gradostat flasks contained simple communities of the water flea Daphnia pulex consuming a mix of three algal species (Pseudokirchneriella subcapitata, Scenedesmus quadricauda, Ankistrodesmus falcatus). This experiment employed a 2x2x2 factorial design to test the importance of ecosystem trophic connectivity (a treatment considering movement of medium only vs. movement of media, phytoplankton and Daphnia between flasks) and metaecosystem configuration (linear or dendritic) on the stability of Daphnia populations and algal communities with two levels of enriched medium input (regular and phosphorus-enriched). Four replicates of this whole design were established, for a total of 32 metaecosystems, run in 9 blocks due to time and space constraints. Each metaecosystem consisted of four “nodes” of 500 mL Erlenmeyer flasks with a foam stopper to allow for gas exchange (128 flasks total), seeded initially with 100 mL algal mix (total average algal density of 2.22 x106 +/- 1.3x104 cells/mL) to which 50 adult Daphnia with eggs (which produce broods of about 15 individuals each week in good conditions (Schwartz 1984) were added before topping off the flask to 500 mL with FLAMES media (Celis-Salgado et al. 2008). Configuration was controlled by unidirectionally connecting flasks in either a linear configuration (in →1→2→3→4→out) or a dendritic configuration (in →1, in→2, 1→3, 2→3, 3→4→out). We chose this as the simplest possible design in which a linear network could be compared to a branched network, with four nodes being the smallest possible number of nodes to create a dendritic configuration, and the two nodes branching into a third, similar to headwater in a river. Flasks were then connected by Tygon tubing and from an inflow reservoir of FLAMES medium (10 μgP/L) or enriched P (70 μgP /L) medium which was pumped through the array of flasks using peristaltic pumps (Watson-Marlow 503S/RL and Rainin Dynamix RP-1). Pumps were set on automatic timers to run for one hour each day at a speed adjusted to move a specific volume of media over that hour. The dilution rate was 10% of the total volume per for all flasks in the linear configurations and the “hub” (3) and “terminal” (4) nodes of the dendritic configurations (50 mL), and 5% per day (25 mL) for the “upstream” nodes in the dendritic configurations (Figure 1). We also controlled functional connectivity, contrasting metaecosystem dynamics when only nutrients moved versus the case when nutrients, resources and consumers moved. To block the flow of organisms in the nutrient-only connectivity treatment, outflow tubing was placed inside an 80-µm nylon mesh held in place with the stopper. Due to colony formation of the phytoplankton and clogging of the mesh, this proved to be an effective retention mechanism also for the algal resources, thus we believe flow of algae was significantly reduced in these treatments compared the trophic connectivity treatments. Though it is possible a small portion of single cells were able to pass through, Scenedesmus is known to form four-cell colonies in the presence of consumers (which we also observed in our algal counts), which are too large to pass through the mesh. As D. pulex were unable to fit through the tubing or survive moving through the peristaltic pumps, in the trophic connectivity treatment, D. pulex were manually moved using a 2mL transfer pipette at a rate of 10% of the population per day (20% were moved after each sampling count as sampling was only done every two days) in all linear nodes and the hub and terminal dendritic nodes, and 5% per day (10% moved after sampling) in the upstream dendritic nodes, in the same downstream direction as media. This type of passive movement at the flow rate of the system would be typical of planktonic animals in rivers that cannot swim upstream. Inflow stock solutions were prepared using FLAMES media (10 μgP/L). Finally, we modified our inflow reservoirs to contain either additionally P-enriched (high P) or regular (low P) FLAMES media. To increase P in the additionally nutrient-enriched treatment without changing pH, 132 μg/L of H2KPO4 and 168 μg/L H2KPO4 were added to our increased P treatment inflow stock solution. For the less-phosphorus treatment, no additional phosphorus was added, but 218 μg/L KCl were added to control for the K added to the high-P medium. See Figure S1 for a photograph of the experimental setup. Experimental Sampling The gradostats were sampled every other day for 30 days. In each node, the concentration of each algal species was measured using a haemocytometer. To estimate Daphnia population size, a 2mL plastic transfer pipette was used to gently agitate, and then sample each node. The number of individuals and two age classes (adult or juvenile) in the pipette were determined and then replaced to the experimental flask. This process was repeated five times, and the average D. pulex count of the five samples was used to estimate Daphnia density/2mL (total number estimated per flask = sampled count average *250). A pilot experiment testing this method proved it had an average error of 17.41 %, equating to 2.5 Daphnia more or less than the expected count at known densities; there is no reason to believe this error was systematic in one direction or the other, or to be systematically biased among our treatments. On Day 30 of the experiment, 40mL samples were taken from each flask to be analysed for total phosphorus concentration (TP). Phosphorus samples were analysed using a standard protocol (Wetzel and Likens 2013) at the GRIL-Université du Québec à Montréal analytical laboratory. Statistical Analysis To quantify the instability of Daphnia populations in experimental gradostats, we determined the peak total Dapnhia population size (as estimated by our density samples) and the coefficient of variation (CV) of Daphnia population size over the course of the experiment. These variables were calculated for each node within each gradostat, as well as in aggregate summed across all nodes for additive Daphnia metapopulation peak and CV. Similarly, population CV and peak density were calculated for each species of alga but we analyse here values based on total algal community density (sum of all species present), as Pseudorkirchinella and Ankistrodesmus were undetectable in most flasks for most of the experiment. Scenedesmus was mostly observed in 4-cell colonies, which is common in the presence of consumers, but we counted the total number of cells, not colonies. All analyses of experimental gradostat data were conducted in R version 4 (Team 2020). Statistical tests of the hypothesis were two-sided and with a level of significance of α=0.05. To determine whether metaecosystem connectivity, configuration and nutrient enrichment, as well as node position (1 upstream to 4 terminal), influenced node Daphnia population instability downstream of the nutrient enrichment source, we analysed the effects of these factors on mean Daphnia population and algal community peak values, on mean Daphnia population and algal community CV log-transformed (natural logarithm) values, and on mean final TP concentrations values, using linear mixed-effects models with the four factors as fixed effects. The mixed model included a random effect for ‘system’ which allowed us to account for a possible clustering in the response variables since the four nodes were connected as metaecosystems. For each of these models, pairwise interactions between factors were tested and terms for non-significant interactions were removed from the final models we report. Assumptions on the model errors (randomness, normality, and homoscedasticity) and the presence of possible influential observations or outliers were assessed with diagnostic plots of the model residuals. Robust standard errors (Huang and Li 2022) were used to adjust for heteroscedasticity. We also measured Daphnia metapopulation and algal metacommunity instability at the scale of the entire metaecosystem. To determine whether metaecosystem connectivity, configuration and nutrient enrichment influenced Daphnia metapopulation and algal metacommunity instability, we analysed the effects of these factors on mean Daphnia metapopulation and algal metacommunity peak values, and on mean CV values, using linear mixed-effects models with the three factors as fixed effects, using the block in which a metaecosystem was run

Many ecosystems are now co-invaded by alien plant and herbivore species. The evolutionary naivety of native plants to alien herbivores can make the plants more susceptible to detrimental effects of herbivory than co-occurring invasive plants, in accordance with the apparent competition hypothesis. Moreover, the invasional meltdown hypothesis predicts that in multiply invaded ecosystems, invasive species can facilitate each other's impacts on native communities. Although there is growing empirical support for these hypotheses, facilitative interactions between invasive plants and herbivores remain underexplored in aquatic ecosystems. Many freshwater ecosystems are co-invaded by aquatic macrophytes and mollusks and simultaneously experience nutrient enrichment. However, the interactive effects of these ecological processes on native macrophyte communities remain an underexplored area. To test these effects, we performed a freshwater mesocosm experiment in which we grew a synthetic native community of three macrophyte species under two levels of invasion by an alien macrophyte Myriophyllum aquaticum (invasion vs. no-invasion) and fully crossed with two levels of nutrient enrichment (enrichment vs. no-enrichment) and herbivory by an invasive snail Pomacea canaliculata (herbivory vs. no-herbivory). In line with the invasional meltdown and apparent competition hypotheses, we found that the proportional above-ground biomass yield of the invasive macrophyte, relative to that of the native macrophyte community, was significantly greater in the presence of the invasive herbivore. Evidence of a reciprocal facilitative effect of the invasive macrophyte on the invasive herbivore is provided by the results showing that the herbivore produced greater egg biomass in the presence than in the absence of M. aquaticum. However, nutrient enrichment reduced the mean proportional above-ground biomass yield of the invasive macrophyte. Our results suggested that herbivory by invader P. canaliculata may enhance invasiveness of M. aquaticum. However, nutrient enrichment of habitats that already harbor M. aquaticum may slow down invasive spread of the macrophyte. Broadly, our study underscores the significance of considering several factors and their interactions when assessing the impact of invasive species, especially considering that many habitats experience co-invasion by plants and herbivores and simultaneously undergo various other disturbances, including nutrient enrichment.