Inflation Rate in Argentina decreased to 36.60 percent in July from 39.40 percent in June of 2025. This dataset provides the latest reported value for - Argentina Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

About this dataset

Context

The dataset illustrates the median household income in Bellbrook, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Bellbrook increased by $150 (0.15%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 8 years and declined for 5 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bellbrook median household income. You can refer the same here

About this dataset

Context

The dataset illustrates the median household income in Stratford, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Stratford increased by $150 (0.40%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 7 years and declined for 6 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Stratford median household income. You can refer the same here

Coal rose to 107.55 USD/T on September 5, 2025, up 0.51% from the previous day. Over the past month, Coal's price has fallen 6.07%, and is down 23.99% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coal - values, historical data, forecasts and news - updated on September of 2025.

About this dataset

Context

The dataset illustrates the median household income in Ramsey, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Ramsey increased by $150 (0.14%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 6 years and declined for 5 years.

https://i.neilsberg.com/ch/ramsey-mn-median-household-income-trend.jpeg" alt="Ramsey, MN median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Ramsey median household income. You can refer the same here

Nickel rose to 15,280 USD/T on September 5, 2025, up 0.13% from the previous day. Over the past month, Nickel's price has risen 0.99%, but it is still 3.86% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Nickel - values, historical data, forecasts and news - updated on September of 2025.

This dataset provides the basic building blocks for the USEEIO v1.1 model and life cycle results per $1 (2013 USD) demand for all goods and services in the model in the producer's price (see BEA 2015). The methodology underlying USEEIO is described in Yang, Ingwersen et al., 2017, with updates for v1.1 described in documentation supporting other USEEIO v1.1 datasets. This dataset is in the form of standard matrices. USEEIOv1.1 uses original names for goods and services, to distinguish them from the sector names provided by BEA which reflect industry names and not commodity names, but the BEA codes are maintained. The main model matrices are in green, A, B, and C; the result matrices are in gold, D, L, LCI, and U. Aggregate data quality scores are presented for B, D and U matrices in peach. Data quality scores use the US EPA data quality asssessment system, see US EPA 2016. Aggregated scores are calculated using a flow-weighted average approach as described in Edelen and Ingwersen 2017. References BEA (2015). Detailed Make and Use Tables in Producer Prices, 2007, Before Redefinitions. Bureau of Economic Analysis. https://www.bea.gov/iTable/index_industry_io.cfm Edelen, A. and W. Ingwersen (2017). "The creation, management and use of data quality information for life cycle assessment." International Journal of Life Cycle Assessment. http://dx.doi.org/10.1007/s11367-017-1348-1 US EPA 2016. Guidance on Data Quality Assessment for Life Cycle Inventory Data. US Environmental Protection Agency, National Risk Management Research Laboratory, Life Cycle Assessment Research Center, Washington, DC. https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=321834 Yang, Y., Ingwersen, W. W., Hawkins, T. R., Srocka, M., & Meyer, D. E. (2017). USEEIO: A new and transparent United States environmentally-extended input-output model. Journal of Cleaner Production, 158, 308-318. http://dx.doi.org/10.1016/j.jclepro.2017.04.150. This dataset is associated with the following publication: Yang, Y., W. Ingwersen, T. Hawkins, and D. Meyer. USEEIO: A new and transparent United States environmentally extended input-output model. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 158: 308-318, (2017).

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

About this dataset

Context

The dataset illustrates the median household income in Henrietta town, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Henrietta town increased by $150 (0.19%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 3 years and declined for 8 years.

https://i.neilsberg.com/ch/henrietta-ny-median-household-income-trend.jpeg" alt="Henrietta, New York median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Henrietta town median household income. You can refer the same here

The USD/RUB exchange rate rose to 82.9063 on September 8, 2025, up 2.04% from the previous session. Over the past month, the Russian Ruble has weakened 4.28%, but it's up by 8.45% over the last 12 months. Russian Ruble - values, historical data, forecasts and news - updated on September of 2025.

Monthly indexes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the current month and previous four months. The base year for the index is 2002=100.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Machinery and equipment price index (MEPI) by Supply Use Product Classification (SUPC). Quarterly Data are available from the first quarter of 1997. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2016=100).

Helsinki Region Travel Time Matrix contains travel time and distance information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by walking, cycling, public transportation and car. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (yhdyskuntarakenteen seurantajärjestelmä) data set. The Helsinki Region Travel Time Matrix is available for three different years:

• 2018
• 2015
• 2013

The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.

The data have been calculated for two different times of the day: 1) midday and 2) rush hour.

The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.

Organization of data

The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).

In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field ‘from_id’ in the text files and the field ‘YKR_ID’ in MetropAccess-YKR-grid shapefile as a common key.

Data structure

The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.

NODATA values have been stored as value -1.

Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t

The fields are separated by semicolon in the text files.

Attributes

• from_id: ID number of the origin grid cell
• to_id: ID number of the destination grid cell
• walk_t: Travel time in minutes from origin to destination by walking
• walk_d: Distance in meters of the walking route
• bike_f_t: Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike
• bike_s_t: Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike
• bike_d:Distance in meters of the cycling route
• pt_r_tt: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home
• pt_r_t: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home
• pt_r_d: Distance in meters of the public transportation route in rush hour traffic
• pt_m_tt: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home
• pt_m_t: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home
• pt_m_d: Distance in meters of the public transportation route in midday traffic
• car_r_t: Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account
• car_r_d: Distance in meters of the private car route in rush hour traffic
• car_m_t: Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account
• car_m_d: Distance in meters of the private car route in midday traffic
• car_sl_t: Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account

METHODS

For detailed documentation and how to reproduce the data, see HelsinkiRegionTravelTimeMatrix2018 GitHub repository.

THE ROUTE BY CAR have been calculated with a dedicated open source tool called DORA (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.

The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).

The whole travel chain (“door-to-door approach”) is taken into account in the calculations:
1) walking time from the real origin to the nearest network location (based on Euclidean distance),
2) average walking time from the origin to the parking lot,
3) travel time from parking lot to destination,
4) average time for searching a parking lot,
5) walking time from parking lot to nearest network location of the destination and
6) walking time from network location to the real destination (based on Euclidean distance).

THE ROUTES BY PUBLIC TRANSPORTATION have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination:
1) possible waiting at home before leaving,
2) walking from home to the transit stop,
3) waiting at the transit stop,
4) travel time to next transit stop,
5) transport mode change,
6) travel time to next transit stop and
7) walking to the destination.

Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.

THE ROUTES BY CYCLING are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.

For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.

The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.

More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.

THE ROUTES BY WALKING were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.

The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).

All calculations were done using the computing resources of CSC-IT Center for Science (https://www.csc.fi/home).

''We introduce the Global rRNA Universal Metabarcoding Plankton database (McNichol and Williams et al., 2025), which consists of 1194 samples covering extensive latitudinal and longitudinal transects, including depth profiles, in all major ocean basins from 2003-2020. Unfractionated (>0.2 µm) seawater DNA samples were amplified using the 515Y/926R universal 3-domain rRNA primers, quantifying the relative abundance of amplicon sequencing variants (ASVs) from Bacteria, Archaea, and Eukaryotes with one denominator. Thus, the ratio of any organism (or group) to any other in a sample is directly comparable to the ratio in any other sample within the dataset, irrespective of gene copy number differences. This obviates a problem in prior global studies that used size-fractionation and different primers for prokaryotes and eukaryotes, precluding comparisons between abundances across size fractions or domains.

Sample Collection Samples were collected by multiple collaborations, which used slightly different sample collection techniques. These collection techniques will be outlined by individual cruise.

For the Atlantic Meridional Transects (AMT 19 and AMT 20), 5–10 L of whole seawater was collected from the sea surface using a Niskin bottle and was then filtered onto 0.22 µm Sterivex Durapore filters (Millipore Sigma, Burlington, MA, USA). Samples were collected by Stephanie Sargeant and Andy Rees, Plymouth Marine Laboratory (PML) as part of the Atlantic Meridional Transect (AMT) (Rees, Smyth and Brotas, 2024) research cruises 19 (2009) and 20 (2010) onboard UK research vessel RRS James Cook (JC039 and JC053 – (Rees, 2010a, 2010b). Sterivex filters were capped and stored in RNAlater® (ThermoFisher) at -80 °C until analysis.

For samples taken in the FRAM Strait (Wietz et al., 2021), whole seawater was collected using Remote Access Samplers (RAS; McLane) on seafloor moorings F4-S-1, HG-IV-S-1, Fevi-34, and EGC-5. Moorings were operated within the FRAM / HAUSGARTEN Observatory covering the West Spitsbergen Current, central Fram Strait, and East Greenland Current as well as the Marginal Ice Zone. RAS performed continuous, autonomous sampling from July 2016 – August 2017 in programmed intervals (weekly to monthly). Nominal deployment depths were 30 m (F4, HG-IV), 67 m (Fevi), and 80 m (EGC). However, vertical movements in the water column resulted in variable actual sampling depths, ranging from 25 to 150 m. Per sampling event, two lots of 500 mL of whole seawater was pumped into bags containing mercuric chloride for fixation. After RAS recovery, the two samples per sampling event were pooled, and approximately 700 mL of pooled water was filtered onto 0.22 µm Sterivex cartridges. Filtered samples were stored at -20°C until DNA extraction. For MOSAiC whole seawater was collected from the upper water via a rosette sampler equipped with Niskin bottles through a hole in the sea ice next to the RV Polarstern. If possible, duplicate samples, with two Niskins per depth were collected during the up-casts near the surface (~5 m), 10 m, chlorophyll max (~20–40 m), 50 m, and 100m. in these Niskins and. 1-4 litres was filtered on to Sterivex-filters (0.22 µm pore size) using a peristaltic pump in a temperature controlled lab at 1°C in the dark, only using red light. The number of Sterivex-filters used per sampling event varied between two during Polar Night and 3-4 during Polar day, depending on the biomass found in the samples. Sterivex filters with were stored at -80°C until further processing took place in the laboratory.

GEOTRACES cruises (Anderson et al., 2014), including transects GA02, GA03, GA10, and GP13, collected whole seawater using a Niskin bottle, filtering 100 mL of whole seawater between the surface and 5601 m onto 0.2 µm 25 mm polycarbonate filters. After filtration, 3 mL of sterile preservation solution (10 mM Tris, pH 8.0; 100 mM EDTA; 0.5 M NaCl) was added, and samples were stored in cryovials at -80°C until DNA extraction.

During the 2017 and 2019 SCOPE (Simons Collaboration on Ocean Processes and Ecology) - Gradients cruises, 0.7-4 L of whole seawater was collected at sea using the ships underway system, which is approximately 7 m below the surface, as well as the rosette sampler for depths between 15 – 125 m by Mary R. Gradoville, Brittany Stewart, and Esther Wing (Zehr lab) (Gradoville et al., 2020). This water was filtered onto 0.22 µm 25 mm Supor membrane filters (Pall Corporation, New York) and stored at -80°C until DNA extraction.

The collection of Southern Ocean transects include the 1) IND-2017 dataset, which were taken during the Totten Glacier-Sabrina Coast voyage in 2017 as part of the CSIRO Marine National Facility RV Investigator Voyage IN2017_V01, 2) the Kerguelen-Axis Marine Science program (K-AXIS) in 2016 on the Australian Antarctic Division RV Aurora Australis 2015/16 voyage 3, 3) Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) P15S cruise in 2016 as a part of the CSIRO Marine National Facility RV Investigator Voyage IN2016_V03, and 4) the Heard Earth-Ocean-Biosphere Interactions (HEOBI) voyage in 2016 as part of the CSIRO Marine National Facility RV Investigator Voyage IN2016_V01. For these cruises, 2 L of whole seawater was filtered onto 0.22 µm Sterivex-GP polyethersulfone membrane filters (Millipore). This water was collected from the ships underway during IND-2017 by Amaranta Focardi (Paulsen Lab, Macquarie University), from between 5 and 4625 m during K-AXIS by Bruce Deagle and Lawrence Clarke (Australian Antarctic Division), from between 5 and 6015 m during GO-SHIP P15S by Eric J. Raes, Swan LS Sow and Gabriela Paniagua Cabarrus (Environmental Genomics Team, CSIRO Environment), Nicole Hellessey (University of Tasmania) and Bernhard Tschitschko (University of New South Wales), and from between 7-3579 m during HEOBI by Thomas Trull (CSIRO Environment). After filtration, samples were stored at -80°C until analysis.

As part of GO-SHIP, there were several additional transects (i.e., I08S, I09N, P16 S/N), including some that also traversed into the Southern Ocean (i.e., I08S, P16S) or Arctic Ocean (P16N). For I08S and I09N, 2 L of whole seawater was filtered onto 0.22 µm 25 mm filters (Supor® hydrophilic polyethersulfone membrane) by Norm Nelson (I08S) and Elisa Halewood (I09N), UCSB, as part of the U.S. Global Ocean Ship-based Hydrographic Investigations Program aboard the R/V Roger Revelle during the cruises in 2007. Sucrose lysis buffer was added to filters, which were then stored at -80°C until DNA extraction. For P16N and P16S, samples were collected at various depths by Elisa Halewood and Meredith Meyers (Carlson Lab, UCSB) onto 0.22 µm 25 mm Supor filters during two latitudinal transects of the Pacific Ocean in 2005 and 2006 as part of the GO-SHIP repeat hydrography program (then known as CLIVAR). Samples were stored as partially extracted lysates in sucrose lysis buffer at -80°C until DNA extraction.

Finally, for samples from the Production Observations Through Another Trans-Latitudinal Oceanic Expedition (POTATOE) cruise, 20 L of whole seawater was collected from the sea surface between 1-2 m and filtered onto 0.22 µm Sterivex® filters during a “ship of opportunity” cruise on the RVIB Nathaniel B Palmer in 2003 (Baldwin et al., 2005). Sterivex filters were stored dry at -80°C until DNA extraction.

All datasets had corresponding environmental data. We included date, time, latitude, longitude, depth, temperature, salinity, oxygen for all transects, and nutrient data where available. However, some cruises have other environmental data which can be found at the British Oceanographic Data Centre https://www.bodc.ac.uk/ for both AMT cruises, at the CSIRO National Collections and Marine Infrastructure Data Trawler https://www.cmar.csiro.au/data/trawler/survey_details.cfm?survey=IN2016_V01 for IND-2017 and HEOBI, at the CLIVAR and Carbon Hydrographic Data Office https://cchdo.ucsd.edu/ for GO-SHIP P15S, P16N and P16S, at the Australian Antarctic Division Datacenter https://data.aad.gov.au/aadc/voyages/ for the K-AXIS cruise, at https://doi.org/10.6075/J0CCHLY9 for the I08S and I09N cruises, at the MGDS (Marine Geoscience Data System: https://www.marine-geo.org) for POTATOE, at https://scope.soest.hawaii.edu/data/gradients/documents/ for both SCOPE-Gradients cruises, and at PANGAEA https://www.pangaea.de/ for FRAM Strait and MOSAiC. Finally, we have also used satellite data to estimate the euphotic zone depth where photosynthetic available radiation (PAR) is 1% of its surface value (Lee et al., 2007; Kirk, 2010). We approximated the euphotic zone depth using the light attenuation at 490nm (Kd 490) product and the relationship Z eu(1%) = 4.6/Kd 490. We also used the script Longhurst-Province-Finder https://github.com/thechisholmlab/Longhurst-Province-Finder to assign each sample to the Longhurst Province in which it was sampled in, another useful column to help subset data and investigate specific regions of the ocean.

DNA Extraction For AMT cruises, DNA was isolated using the Qiagen AllPrep DNA/RNA Mini kit (Hilden, Germany) with modifications to be compatible with RNAlater® and to disrupt cell membranes (Varaljay et al., 2015). Briefly, the filter was removed from the Sterivex housing and immersed in RLT+ buffer that had been amended with 10 µl 1N NaOH per 1ml buffer, followed by a 2 minute agitation in a Mini-Beadbeater-96 (Biospec Inc., Bartlesville, OK, USA) with 0.1- and 0.5 mm sterile glass beads

Original provider: Reny Tyson, Duke University, and Brian Balmer, Chicago Zoological Society, c/o Mote Marine Laboratory

Dataset credits: Reny Tyson, Duke University Brian Balmer, Chicago Zoological Society, c/o Mote Marine Laboratory

Abstract: We examined bottlenose dolphin Tursiops truncatus community structure and abundance in the northeast Gulf of Mexico coastal waters stretching from St. Vincent Sound to Alligator Harbor, Florida, USA. Photographic-identification surveys were conducted between May 2004 and October 2006 to gain an understanding of dolphin distribution in this region. Dolphins were distributed year-round throughout the region; however, individual sighting records indicate that 2 parapatric dolphin communities exist. We conducted mark-recapture surveys using photographic-identification techniques to estimate the abundance of dolphins inhabiting the 2 areas these communities reside in: St. Vincent Sound/Apalachicola Bay, western; and St. George Sound/Alligator Harbor, eastern. Sighting records of individual dolphins from 2004 to 2008 support the existence of 2 communities in these areas; only 3.5% of distinctive dolphins photographed were seen in both western and eastern areas. The 2 communities differ in their structure: the eastern area supports a more transient population with 45.7% of distinctive dolphins photo graphed only once compared with 28.3% in the west. Independent estimates of abundance (N, 95% CI = [low, high]) were calculated using the Chapman modification of the Lincoln-Petersen method for June 2007 and for January and February 2008 for the eastern area (242 [141−343], 395 [273−516]) and for the western survey area (197 [130−264], 111 [71−150]), respectively. Our results serve as a baseline that can be used by the US National Marine Fisheries Service to manage bottlenose dolphins in this region.

Purpose: not provided

Supplemental information: [2015-03-24] A few records had a wrong animal count of zero. The value is replaced with a blank representing species presence only.

The benchmark interest rate in Turkey was last recorded at 43 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

This seismic data set was recorded in September 2015 on board the R/V Pourquoi Pas? during the GHASS cruise (IFREMER) on the western Black Sea, offshore Romania. The available profiles (part of the collected data) within this deposit are located on the continental slope, bathymetry between 500 to 1200 meters. The pre-processing of the seismic data included common midpoint binning @6.25 meters, trace and shot edition, source delay correction, and a 35-375 Hz band pass filtering. Detailed Root Mean Square Velocity analyses were performed on semblance panels computed using super gathers every 150 m. Normal Move Out time correction was then applied on the Common Mid Point (CMP) using these velocities prior to stack. Interval velocities were computed using the Dix equation. The velocity model was then interpolated every CMP location, converted to depth and smoothed to perform post-stack depth migration. The depth migrated sections and the depth velocity models have been output to standard SEG-Y rev1 format (https://library.seg.org/pb-assets/technical-standards/seg_y_rev1-1686080991247.pdf) with values written using “big-endian” byte ordering and IEEE floating-point. For a given profile, both SEGY files have the same number of traces and the same bin locations. Velocity unit is in meter.second-1. The depth sampling is set to 0.5 meter for both files. The recording delay is zero for the depth migration SEGY files. The delay is coded in meters and constant for a given depth velocity SEGY file, stored within the Trace Header (bytes 109-110). Trace coordinates are also stored within the Trace Header using WGS84 +DDDMMSS.ss format with a scale factor of -100 (bytes 81-88, which means that the value has to divided by 100) . For more convenient access to the location of the profiles, these coordinates are also stored into ASCII files using decimal degrees.