This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 166 data sources representing a total of 1676 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. An additional table presents all references to the data sources, and, if applicable, the open access license under which these are published. When using (parts of) this data set, please respect the original access licenses. This data set underlies all analyses performed in 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes. Tables for calculating trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References'. In the table 'PlotData' more details on each site within a dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses (except for column 'Stratum', which is derived from table "SampleData'). Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Context

Podcast exists for near two decades. But it really takes over in recent two years. The podcast meta data may be useful for research in fields like machine learning, social science, or media in general.

Content

Listen Notes is the podcast search engine that actually works. It has the most comprehensive podcast database that you can find on the Internet.

This dataset includes the meta data of (almost) all podcast episodes that were published in December 2017.

If you are building apps / online services that need to access THE podcast database (i.e., all podcasts and all episodes o the Internet), then you can try our Listen API: Podcast Search & Directory API. It's freemium model and pay as you go.

You are able to get the most up-to-date podcast meta data in CSV files or SQLite files.

Listen Notes also has some interesting podcast stats.

Data source: RSS feed of podcasts.

Acknowledgements

Thanks for all the podcasters who produce those inspiring / entertaining shows.

Inspiration

N/A

Trust in mainstream institutions is declining while people are increasingly turning to alternative media and conspiracy theories. Previous research has suggested that these trends may be linked, but the dynamics of trust across multiple sources has received little investigation. Is trust a neutral process, where each source is judged independently, is it a zero-sum competition, where a loss for one side is a gain for the other, or does losing trust in one source in foster a more generalized sense of distrust? Across three experimental studies (N = 2,951) we examined how people react when a source makes a serious error, testing four potential models of trust dynamics. We found that regardless of whether the outlet is mainstream, counter-mainstream, or neutral, trust drops for the erring source but does not rise for its competitors. This was the case in the context of both food regulations and COVID-19 precautions. Such a pattern suggest that each source may be judged independently of others. However, in several cases, an error made by one source led to a loss of trust in all sources, suggesting that rather than choosing sides between competing sources, people are also judging the media landscape as a whole to discern if it is feasible to find trustworthy information. However, correlational data did also find that the more people saw a source as politicized, the less they trusted that source and the more they trusted its competitors.

UPDATED on October 15 2020 After some mistakes in some of the data were found, we updated this data set. The changes to the data are detailed on Zenodo (http://doi.org/10.5281/zenodo.4061807), and an Erratum has been submitted. This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 165 data sources, representing a total of 1668 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets. When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'. In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses. Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

The Global Alien Species First Record Database represents a compilation of first records of alien species across taxonomic groups and regions.

A first record denotes the year of first observation of an alien species in a region. Note that this often differs from the date of first introduction. The database covers all regions (mostly countries and some islands) globally with particularly intense sampling in Europe, North America and Australasia. First records were gathered from various data sources including online databases, scientific publications, reports and personal collections by a team of >45 researchers. A full list of data sources, an analysis of global and continental trends and more details about the data can be found in our open access publication: Seebens et al. (2017) No saturation in the accumulation of alien species worldwide. Nature Communications 8, 14435.

Note that species names and first records may deviate from the original information, which was necessary to harmonise data files. Original information is provided in the most recent files.

Note that first records are sampled unevenly in space and time and across taxonomic groups, and thus first records are affected by sampling biases. From our experience, analyses on a continental or global scale are rather robust, while analyses on national levels should be interpreted carefully. For national analyses, we strongly recommend to consult the original data sources to check sampling methods, quality etc individually.

The first record database will be irregularly updated and the most recent version is indicated by the version number. _Newer Versions_ are accessible via Zenodo_: https://doi.org/10.5281/zenodo.10039630

Here, we provide several files: (1) The annual number of first records per taxonomic group and continent in an excel file, which represents the aggregated data used for most of the analyses in our paper (Seebens et al. Nat Comm). (2) The R code for the implementation of the invasion model used in the paper. (3) A more detailed data set with the first records of individual species in a region. This data set represents only a subset (~77%) of the full database as some data were not publicly accessible. This data set will be irregularly updated and may differ from the data set used in our paper. All data are free of use for non-commercial purposes with proper citation of Seebens et al. (2017) Nat Comm 8, 14435. (4) A substantially updated version of the First Record Database (vs 1.2) used in our second publication: Seebens et al. (2018) Global rise in emerging alien species results from increased accessibility of new source pools. PNAS 115(10), E2264-E2273.

Please, do not ask the contact person for data, but download it at Zenodo: https://doi.org/10.5281/zenodo.10039630 - Thanks!

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is a global ocean marine meteorological and surface ocean dataset. It is formed by merging many national and international data sources that contain measurements and visual observations from ships (merchant, navy, research), moored and drifting buoys, coastal stations, and other marine platforms. Each report contains individual observations of meteorological and oceanographic variables, such as sea surface and air temperatures, wind, pressure, humidity, and cloudiness. The coverage is global and sampling density varies depending on date and geographic position relative to shipping routes and ocean observing systems. All three U.S. ICOADS partners (NOAA/ESRL, NOAA/NCDC, NCAR) offer various data access and format options. To review all available options see the ICOADS website [http://icoads.noaa.gov/products.html].

IMPORTANT: The time period of data available is defined in two segments. * ICOADS Release 2.5 covers 1662 through 2007 * All data following the Release 2.5 end date is based exclusively on real-time GTS data with minimal quality control. These data should be considered preliminary and will be subject to change in new Releases of ICOADS

Description

unarXive is a scholarly data set containing publications' full-text, annotated in-text citations, and a citation network.

The data is generated from all LaTeX sources on arXiv and therefore of higher quality than data generated from PDF files.

Typical use cases are

Citation recommendation

Citation context analysis

Bibliographic analyses

Reference string parsing

This version (v3) of our data set is based on all arXiv publications until 2020-07-31 and on the Microsoft Academic Graph as of 2020-08-18. As additional contribution, we included a table with the publication date and the scientific discipline for each paper for easier filtering.

Note: This Zenodo record is an old version of unarXive. You can find the most recent version at https://zenodo.org/record/7752754 and https://zenodo.org/record/7752615

Access

┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓┃ D O W N L O A D S A M P L E  ┃┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛

To download the whole data set send an access request and note the following:

Note: this Zenodo record is a "full" version of unarXive, which was generated from all of arXiv.org including non-permissively licensed papers. Make sure that your use of the data is compliant with the paper's licensing terms.¹

¹ For information on papers' licenses use arXiv's bulk metadata access.

The code used for generating the data set is publicly available.

Usage examples for our data set are provided at here on GitHub.

Citing

This initial version of unarXive is described in the following journal article.

Tarek Saier, Michael Färber: "unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata", Scientometrics, 2020,[link to an author copy]

The updated version is described in the following conference paper.

Tarek Saier, Michael Färber. "unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network", JCDL 2023.[link to an author copy]

Dataset Description: Underwater Sensor Data from River

1. Data Source: The dataset comprises sensor readings obtained from underwater equipment deployed in a river environment. The sensors are designed to monitor various environmental parameters to provide insights into the river's conditions.

2. Data Fields:

   • Meta Data:
     • Source Address: The IP address of the device collecting the data (e.g., 192.168.0.1).
     • Destination Address: The IP address of the receiver or data storage system (e.g., 192.168.0.2).
     • Timestamp: The date and time when the data was recorded (e.g., 2023-08-17 18:28:00).
   • Environmental Parameters:
     • Temperature: The water temperature in degrees Celsius (e.g., 22.8°C).
     • Pressure: The pressure exerted by the water in millibars (e.g., 1011.0 mb).
     • Oxygen Level (O2): The percentage of dissolved oxygen in the water (e.g., 39.7%).
   • Obstacle Sensor Data: Indicates whether any obstacles are detected in the vicinity of the sensor equipment. This field provides binary information, signaling either the presence or absence of obstacles.

3. Data Collection Context: The sensors are deployed in a river environment to monitor and gather real-time data on crucial parameters. The collected data aids in understanding the river's ecosystem, assessing water quality, and detecting potential hazards such as obstacles or blockages.

4. Data Use Cases:

   • Environmental Monitoring: Researchers and environmentalists can utilize the dataset to study long-term trends in water temperature, oxygen levels, and pressure, facilitating assessments of the river's health and changes over time.
   • River Management: Authorities responsible for managing rivers can leverage the dataset to make informed decisions regarding resource allocation, pollution control measures, and infrastructure maintenance.
   • Research and Innovation: The dataset can serve as a valuable resource for academic research, enabling the development of advanced algorithms for obstacle detection, predictive modeling of water quality parameters, and more.

5. Data Integrity and Quality: Measures are taken to ensure the accuracy and reliability of the collected data. Calibration routines, quality control checks, and redundancy mechanisms may be implemented to minimize errors and maintain data integrity.

6. Ethical Considerations: Data collection adheres to ethical guidelines, ensuring minimal disturbance to the natural environment and compliance with relevant regulations governing data privacy and environmental protection.

7. Data Access and Availability: Access to the dataset may be restricted to authorized parties, such as researchers, governmental agencies, and environmental organizations. However, efforts may be made to promote data sharing and collaboration within the scientific community while respecting confidentiality and security protocols.

8. Maintenance and Updates: The dataset may be periodically updated with new observations as additional data is collected over time. Maintenance tasks, including sensor calibration, equipment servicing, and data validation, are conducted to sustain the dataset's reliability and relevance.

Background: Critical care units (CCUs) with wide use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like Clinical Information System (CIS), Laboratory Information Management System (LIMS), etc. These systems are proprietary in nature, allow limited access to their database and have vendor specific clinical implementation. In this study we focus on developing an open source web-based meta-data repository for CCU representing stay of patient with relevant details.

Methods: After developing the web-based open source repository we analyzed prospective data from two sites for four months for data quality dimensions (completeness, timeliness, validity, accuracy and consistency), morbidity and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators. Results: Data dictionary (DD) with 1447 fields (90.39% categorical and 9.6% text fields) is presented to cover clinical workflow of NICU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 82% completeness, 97% accuracy, 91% timeliness and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicator and practice variations are strongly correlated (p-value < 0.05).

Results: Data dictionary (DD) with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 82% completeness, 97% accuracy, 91% timeliness and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (p-value < 0.05).

Conclusion: This study documents DD for standardized data collection in CCU. This provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.

In order to utilize the data, we provide a MATLAB interface. It can be accessed via GitHub repository at https://github.com/KIT-3DUSCT/3DUSCT-data-access-script. The source code is provided under 3-clause BSD-license. The interface software enables retrieving A-scans, emitter and receiver positions, and additional meta data as e.g. ultrasound pulse information, temperature distribution in water. For questions and bug reports regarding the MATLAB interface please use the GitHub issue tracker. Further information on the experimental setup (sytem description) are available on the webpage http://ipeusctdb1.ipe.kit.edu/~usct/challenge/?page_id=92

A global survey conducted in the third quarter of 2024 found that the main reason for using social media was to keep in touch with friends and family, with over 50.8 percent of social media users saying this was their main reason for using online networks. Overall, 39 percent of social media users said that filling spare time was their main reason for using social media platforms, whilst 34.5 percent of respondents said they used it to read news stories. Less than one in five users were on social platforms for the reason of following celebrities and influencers.

              The most popular social network

              Facebook dominates the social media landscape. The world's most popular social media platform turned 20 in February 2024, and it continues to lead the way in terms of user numbers. As of February 2025, the social network had over three billion global users. YouTube, Instagram, and WhatsApp follow, but none of these well-known brands can surpass Facebook’s audience size.
              Moreover, as of the final quarter of 2023, there were almost four billion Meta product users.

              Ever-evolving social media usage

              The utilization of social media remains largely gratuitous; however, companies have been encouraging users to become paid subscribers to reduce dependence on advertising profits. Meta Verified entices users by offering a blue verification badge and proactive account protection, among other things. X (formerly Twitter), Snapchat, and Reddit also offer users the chance to upgrade their social media accounts for a monthly free.

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.