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All gillnet data represented here expand upon vessel operations (OP table) data, all of which are collected by the United States Geological Survey, Great Lakes Science Center and its partners. The Gillnet Tables contain data collected from the research vessel deploying various gear used for gillnet data collection. The database uses sample_type to indicate the gear deployed. The tables relating to Gillnet are: GN_Annulus.csv, GN_Catch.csv, GN_Effort.csv, GN_Fish.csv, GN_L, GN_LF.csv, GN_OP.csv, GN_Prey.csv, GN_Stomach.csv, LMMB_Fish_Prey.csv, and LMMB_Invert_Prey.csv Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being...
The USGS Great Lakes Science Center will use ScienceBase to manage information, including archival science records, select data sets, citations for possible dissemination and sharing via web feeds. Advancing scientific knowledge and providing scientific information for restoring, enhancing, managing, and protecting the living resources and their habitats in the Great Lakes basin ecosystem. More information available at: http://www.glsc.usgs.gov/
All mensuration data represented here expand upon vessel operations (OP table) data, all of which are collected by the United States Geological Survey, Great Lakes Science Center and its partners. The Mensuration Tables contain data collected from the research vessel deploying various gear used for mensuration data collection. The database uses sample_type to indicate the gear deployed. The tables relating to Mensuration are: Mensuration.csv, MS_head_rope_depth.csv, MS_FOOT_ROPE_DEPTH, MS_Primary.csv, MS_Temperature.csv, and MS_Wingspread.csv Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being undertaken after the time...
One of the highest priorities in carbon sequestration science is the development of techniques for CO2 separation and capture, because it is expected to account for the majority of the total cost ( 75%). The most common currently used method of CO2 separation is reversible chemical absorption using monoethanolamine (MEA) solvent. In the current study, solvent degradation from this technique was studied using degraded MEA samples from the IMC Chemicals Facility in Trona, California. A major pathway to solvent degradation that had not been previously observed in laboratory experiments has been identified. This pathway, which is initiated by oxidation of the solvent, is a much more significant source of solvent degradation...
USGS Historical Quadrangle in GeoPDF.
The Great Lakes Research Vessel Catch (RVCAT) database is managed at the Great Lakes Science Center (GLSC). RVCAT has been used as the primary data collection tool aboard the GLSC’s research vessel operations across the Great Lakes. Content: The data set has been collected on various vessel operations on all the Great Lakes and select connecting waterways between the years 1930-2021. Data collection begins in early spring and ends in late fall. Each vessel operation was completed for a specific purpose, or target mission. Data Structure: These data are collected at the GLSC in the RVCAT Oracle database. This database has been broken down into comma separated value (csv) spreadsheets in order to facilitate greater...
These data have been collected on various vessel operations on the Great Lakes and select connecting waterways. This vessel operations data set is part of and connected to a larger database of Great Lakes research that includes trawl and gillnet catch data, sample information of fish species caught, as well as effort applied, operation conditions, and location details. The larger database also contains data on hydroacoustics, ichthyoplankton, zooplankton, zebra mussel, and benthos samples collected. Multiple operations were conducted on all Great Lakes each year (1958-2016) beginning in early spring and ending in late fall. Each vessel operation was completed for a specific purpose, or target mission, which are...
The Great Lakes Research Vessel Operations data release is taken from the Research Vessel Catch (RVCAT) database curated at the Great Lakes Science Center (GLSC). RVCAT has been used as the primary data collection tool aboard the GLSC’s research vessel operations. Content: The data set has been collected on various vessel operations on all the Great Lakes and select connecting waterways between the years 1958-2018. Data collection begins in early spring and ends in late fall. Each vessel operation was completed for a specific purpose, or target mission, which are enumerated in this data set. In addition to vessel operations data, RVCAT collects trawl and gillnet catch data, sample information of fish species caught,...
All benthos data represented here expand upon vessel operations (OP table) data, all of which are collected by the United States Geological Survey, Great Lakes Science Center and it’s partners. The Benthos Tables contain data collected from the research vessel deploying various gear used for benthos data collection. The database uses sample_type to indicate the gear deployed. The tables relating to Benthos are: Benthos_Comments.csv, Benthos_Ponar.csv, and Benthos_LF Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being undertaken after the time of release. Also, a large section of this database includes legacy data that...
All mysis data represented here expand upon vessel operations (OP table) data, all of which are collected by the United States Geological Survey, Great Lakes Science Center and its partners. The Mysis Tables contain data collected from the research vessel deploying various gear used for mysis data collection. The database uses sample_type to indicate the gear deployed. The tables relating to Mysis are: Mysis_Catch.csv, Mysis_IND.csv, Mysis_OP.csv, and MY_IND Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being undertaken after the time of release. Also, a large section of this database includes legacy data that if issues...
All trawl data represented here expand upon vessel operations (OP table) data, all of which are collected by the United States Geological Survey, Great Lakes Science Center and its partners. The Trawl Tables contain data collected from the research vessel deploying various gear used for trawl data collection. The database uses sample_type to indicate the gear deployed. The tables relating to Trawl are: Bucket.csv, Prey_Length.csv, Prey_Total.csv, TR_Annulus.csv, TR_Catch.csv, TR_Fish.csv, TR_L.csv, TR_LF.csv, TR_LMMB_Fish_Prey.csv, TR_LMMB_Invert_Prey.csv, TR_OP.csv, TR_Prey.csv, TR_Sub.csv, sub_prey_total.csv, and sub_prey_length.csv Data Quality: Note that the following data release is a snapshot of the database...
The RVCAT database contains data that have been collected on various vessel operations on the Great Lakes and select connecting waterways. This section of Reference Tables specifically handles repetitive or standardized information that is called upon in the main tables of the RVCAT database. Reference tables are used in database design in order to standardize often used values and to make the data file efficient. All of the terms defined in the reference tables have been determined by the United States Geological Survey, Great Lakes Science Center and it’s partners. Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being...
This paper intends to present a contribution to the development of techniques of decision-making in international projects of exploration and production of petroleum. A case study of the Multi-Attribute Utility Theory (MAUT) was developed and applied to define priorities among ten offshore exploration investment opportunities around the world offered to a Brazilian petroleum multinational company, according to decision-makers’ preferences. The MAUT model proposed shows for the first time a combination of operational, political and technological risks, resulting in a quite complete formulation to describe quantitatively the decision-making process. It is important to emphasize that this paper presents an unpublished...
USGS Historical Quadrangle in GeoPDF.
This paper intends to present a contribution to the development of techniques of decision-making in international projects of exploration and production of petroleum. A case study of the Multi-Attribute Utility Theory (MAUT) was developed and applied to define priorities among ten offshore exploration investment opportunities around the world offered to a Brazilian petroleum multinational company, according to decision-makers’ preferences. The MAUT model proposed shows for the first time a combination of operational, political and technological risks, resulting in a quite complete formulation to describe quantitatively the decision-making process. It is important to emphasize that this paper presents an unpublished...
USGS Historical Quadrangle in GeoPDF.
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