cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp/jci7sr
title: Canadian Business Patterns, June 2013 [B2020]

description: The Canadian Business Patterns contains data that reflects counts of business locations (as of December 2008) and business establishments (prior to December 2009) by: 9 employment size ranges, including "indeterminate" (as of December 1997); geography groupings: province/territory, census division, census subdivision (before December 2008), census metropolitan area and census agglomeration; and industry using the North American Industry Classification System (tables at the 2, 3, 4 and 6-digit level) as of December 1998. Before December 2004, these data were also presented using the Standard Industrial Classification (tables at the 1, 2, 3 and 4-digit level). The data published in the Canadian Business Patterns represents the current number of locations or establishments for a specific reference period which is taken from the Business Register Central Frame Data Base. It is not intended for use as a time series because changes that affect the continuity of the data might resu lt from changes in methodology. Some examples are: the change to another version of the Standard Geographical Classification (SGC) or the North American Industry Classification System (NAICS), the addition of the new territory of Nunavut and new rules to better identify inactive units.
DOI: 10.5683/sp/r5edqt
title: Canadian Business Patterns, June 2012 [B2020]

description: The Canadian Business Patterns contains data that reflects counts of business locations (as of December 2008) and business establishments (prior to December 2009) by: 9 employment size ranges, including "indeterminate" (as of December 1997); geography groupings: province/territory, census division, census subdivision (before December 2008), census metropolitan area and census agglomeration; and industry using the North American Industry Classification System (tables at the 2, 3, 4 and 6-digit level) as of December 1998. Before December 2004, these data were also presented using the Standard Industrial Classification (tables at the 1, 2, 3 and 4-digit level). The data published in the Canadian Business Patterns represents the current number of locations or establishments for a specific reference period which is taken from the Business Register Central Frame Data Base. It is not intended for use as a time series because changes that affect the continuity of the data might resu lt from changes in methodology. Some examples are: the change to another version of the Standard Geographical Classification (SGC) or the North American Industry Classification System (NAICS), the addition of the new territory of Nunavut and new rules to better identify inactive units.
DOI: 10.48366/r642098
title: Object Detection using Event Camera 

description: Object detection using event cameras is a unique and emerging field that leverages neuromorphic sensors to capture visual information in a fundamentally different way than traditional frame-based cameras. Event cameras, also known as dynamic vision sensors (DVS) or neuromorphic cameras, operate based on changes in luminance (brightness) rather than capturing frames at fixed intervals. Here's a comparison of object detection using event cameras.
DOI: 10.34740/kaggle/dsv/6565803
title: Brazil Import/Export data from 1996 to 2023
DOI: 10.5281/zenodo.8388083
title: parotocinclus_cabessadecuia_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8388082
title: parotocinclus_cabessadecuia_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8264922
title: UTexas Aptamer Dataset August 2023

description: The deposited dataset is a snapshot of the data in the active and growing UTexas Aptamer Database, https://sites.utexas.edu/aptamerdatabase/. This dataset is a collection of aptamer data that has been extracted from the literature every year since the inception of aptamer selections and includes multiple aptamer sequences from a given paper (as opposed to just sequences with the tightest binding). In all, the collection includes 1,415 aptamer sequences from 489 papers published over the last few decades (1990-2022). Since our dataset includes multiple sequences that emerged from a given selection experiment, it of necessity includes sequences that may not have been individually tested for binding activity, similar to the inclusion of all rRNA sequences in a metagenomic analysis of an environmental sample. By taking this metagenomic approach, we provide informaticians with a much wider range of sequences for subsequent analysis while still providing tools to find high-affinity aptamers for future use. For each aptamer sequence, the dataset includes information about the aptamer publication (i.e., year of publication, DOI, full citation, and corresponding author(s)), the aptamer target, as well as the following information about the specific aptamer: nucleic acid composition, name assigned in the original publication, sequence, GC percentage, sequence length, binding affinity (K<sub>d</sub>), binding/selection buffer, application as quoted in the referenced paper (e.g., drug delivery, biosensor, etc.), original nucleic acid pool used in the aptamer selection, post-selection modifications (if any), additional information, and our internally assigned serial number. We used simple Excel formulas for each aptamer record to calculate the GC content and length of each aptamer sequence.
description: This research was funded by The University of Texas Freshman Research Initiative, which was supported by the Howard Hughes Medical Institute (#52008124, concluded in 2021), and the College of Natural Sciences. Ali Akari's work was partially supported by the UT-Austin S-STEM Sophomore Scholars Program, which is funded by the National Science Foundation Award (#1742548).
DOI: 10.5683/sp/nheoeg
title: Supply and Use Tables, 2012 [Canada] [Excel]

description: The supply and use tables focus on measuring the productive structure of the economy. They trace production of commodities by domestic industries, combined with imports, through their use as intermediate inputs or as final consumption, investment or exports. The system provides a measure of value added by industry-total output (or sales) less intermediate inputs. These tables can be used to calculate economy-wide gross domestic product (GDP) either directly, by summing value added over the industries, or indirectly, by summing to the economy-wide cost of primary inputs (income-based GDP) or by computing the grand total of the flow of products into final demand categories (expenditure-based GDP)-the link to the national income and expenditure accounts. While the supply and use tables closely reflect actual economic transactions, certain analytical and modeling purposes, however, require symmetric industry-by-industry tables. These symmetric industry-by-industry tables are referred to as input-output tables. The input-output tables show the inter-industry transactions, that is, all purchases of an industry from all other industries, including expenditures on imports and inventory withdrawals, as well as all expenditures on primary inputs. Similarly, the symmetric final demand table shows all purchases by a final demand category from all other industries, including expenditures on imports and inventory withdrawals as well as all expenditures on indirect taxes. The input-output tables allow the analyst to explore "what if?" questions at a fairly detailed level, exploring the impact of exogenous changes in final demand on output while taking account of the interdependencies between different industries and regions of the economy and the leakages to imports and taxes. For example, such models might be used to study the question: "If Canadian oil and gas exports doubled, what industries would be most affected and in which provinces"? The use of an input-output model to address such a question would permit the estimation of indirect, and possibly also some of the induced effects of a demand shock of this nature, and the calculation of the corresponding multipliers. Input-output models were originally developed in the 1930s by Wassily Leontief, a Russian-American who earned the Nobel Prize in Economic Sciences for this work in 1973. His models were inspired by earlier studies by François Quesnay on the "Tableau économique" in 1758 and Léon Walras on general equilibrium theory in 1874. Leontief's models simplified earlier formulations by assuming that the proportions of industry inputs to industry outputs are fixed in the short-term, with no substitutability among any of the intermediate or factor inputs.
DOI: 10.5683/sp/fj0jww
title: Supply and Use Tables, 2013 [Canada] [Excel]

description: The supply and use tables focus on measuring the productive structure of the economy. They trace production of commodities by domestic industries, combined with imports, through their use as intermediate inputs or as final consumption, investment or exports. The system provides a measure of value added by industry-total output (or sales) less intermediate inputs. These tables can be used to calculate economy-wide gross domestic product (GDP) either directly, by summing value added over the industries, or indirectly, by summing to the economy-wide cost of primary inputs (income-based GDP) or by computing the grand total of the flow of products into final demand categories (expenditure-based GDP)-the link to the national income and expenditure accounts. While the supply and use tables closely reflect actual economic transactions, certain analytical and modeling purposes, however, require symmetric industry-by-industry tables. These symmetric industry-by-industry tables are referred to as input-output tables. The input-output tables show the inter-industry transactions, that is, all purchases of an industry from all other industries, including expenditures on imports and inventory withdrawals, as well as all expenditures on primary inputs. Similarly, the symmetric final demand table shows all purchases by a final demand category from all other industries, including expenditures on imports and inventory withdrawals as well as all expenditures on indirect taxes. The input-output tables allow the analyst to explore "what if?" questions at a fairly detailed level, exploring the impact of exogenous changes in final demand on output while taking account of the interdependencies between different industries and regions of the economy and the leakages to imports and taxes. For example, such models might be used to study the question: "If Canadian oil and gas exports doubled, what industries would be most affected and in which provinces"? The use of an input-output model to address such a question would permit the estimation of indirect, and possibly also some of the induced effects of a demand shock of this nature, and the calculation of the corresponding multipliers. Input-output models were originally developed in the 1930s by Wassily Leontief, a Russian-American who earned the Nobel Prize in Economic Sciences for this work in 1973. His models were inspired by earlier studies by François Quesnay on the "Tableau économique" in 1758 and Léon Walras on general equilibrium theory in 1874. Leontief's models simplified earlier formulations by assuming that the proportions of industry inputs to industry outputs are fixed in the short-term, with no substitutability among any of the intermediate or factor inputs.
DOI: 10.5683/sp/2eaytx
title: Canadian Business Patterns, June 2007 [B2020]

description: The Canadian Business Patterns contains data that reflects counts of business locations (as of December 2008) and business establishments (prior to December 2009) by: 9 employment size ranges, including "indeterminate" (as of December 1997); geography groupings: province/territory, census division, census subdivision (before December 2008), census metropolitan area and census agglomeration; and industry using the North American Industry Classification System (tables at the 2, 3, 4 and 6-digit level) as of December 1998. Before December 2004, these data were also presented using the Standard Industrial Classification (tables at the 1, 2, 3 and 4-digit level). The data published in the Canadian Business Patterns represents the current number of locations or establishments for a specific reference period which is taken from the Business Register Central Frame Data Base. It is not intended for use as a time series because changes that affect the continuity of the data might resu lt from changes in methodology. Some examples are: the change to another version of the Standard Geographical Classification (SGC) or the North American Industry Classification System (NAICS), the addition of the new territory of Nunavut and new rules to better identify inactive units.
DOI: 10.5683/sp/viezbd
title: Canadian Business Patterns, June 2006 [B2020]

description: The Canadian Business Patterns contains data that reflects counts of business locations (as of December 2008) and business establishments (prior to December 2009) by: 9 employment size ranges, including "indeterminate" (as of December 1997); geography groupings: province/territory, census division, census subdivision (before December 2008), census metropolitan area and census agglomeration; and industry using the North American Industry Classification System (tables at the 2, 3, 4 and 6-digit level) as of December 1998. Before December 2004, these data were also presented using the Standard Industrial Classification (tables at the 1, 2, 3 and 4-digit level). The data published in the Canadian Business Patterns represents the current number of locations or establishments for a specific reference period which is taken from the Business Register Central Frame Data Base. It is not intended for use as a time series because changes that affect the continuity of the data might resu lt from changes in methodology. Some examples are: the change to another version of the Standard Geographical Classification (SGC) or the North American Industry Classification System (NAICS), the addition of the new territory of Nunavut and new rules to better identify inactive units.
DOI: 10.5517/ccdc.csd.cc2cj1yx
title: CCDC 2190179: Experimental Crystal Structure Determination

description: Related Article: Hosahalli S. Udaya, Vishnu Mishra, Tullimilli Y. Gopalakrishna, Venkataramanarao G. Anand|2023|Org.Lett.|25|6628|doi:10.1021/acs.orglett.3c02328
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjptf
title: CCDC 2190795: Experimental Crystal Structure Determination

description: Related Article: Nerina Shahi, Paras Nath Yadav, Upendra Chaudhary, Mohd Saad, Kuldeep Mahiya, Arif Khan, Syed Shafi, Yuba Raj Pokharel|2023|ACS Omega|8|31998|doi:10.1021/acsomega.3c03824
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjpvg
title: CCDC 2190796: Experimental Crystal Structure Determination

description: Related Article: Nerina Shahi, Paras Nath Yadav, Upendra Chaudhary, Mohd Saad, Kuldeep Mahiya, Arif Khan, Syed Shafi, Yuba Raj Pokharel|2023|ACS Omega|8|31998|doi:10.1021/acsomega.3c03824
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dnx88
title: CCDC 2224630: Experimental Crystal Structure Determination

description: Related Article: Ze Xu, Tianyang Hou, Feng Yang, Linan Zhang, Xiaopeng Zhang, Wei Liu, Qing Lang, Ming Lu, Yuangang Xu|2023|ACS Applied Materials and Interfaces|15|41580|doi:10.1021/acsami.3c09652
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dnx99
title: CCDC 2224631: Experimental Crystal Structure Determination

description: Related Article: Ze Xu, Tianyang Hou, Feng Yang, Linan Zhang, Xiaopeng Zhang, Wei Liu, Qing Lang, Ming Lu, Yuangang Xu|2023|ACS Applied Materials and Interfaces|15|41580|doi:10.1021/acsami.3c09652
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5287/ora-pp4y9nkoz
title: A Functional trait database of arable weeds from Eurasia and North Africa

description: The functional traits of arable weeds in Eurasia and North Africa database contains functional trait values for specific leaf area, canopy height, canopy diameter, leaf area per node thickness, vegetative propagation, and plant life history for 928 arable weed species. The number of accessions used to calculate the trait values are provided. The data was collected up until 2021 from locations around Europe, Western Asia and North Africa, with minor collections in other parts of Eurasia (e.g., Japan), in order to characterise species growing as arable weeds in cereal and pulse crops managed under traditional agricultural regimes (that is, organically, without chemical fertilisers and herbicides, and in some cases minimal mechanisation) in these regions. This database iteration is version 1. The data are used by the R package WeedEco[1] to assign functional trait values to inputted species from archaeobotanical samples or other sources, allowing comparison through discriminant to three arable regime models. Details on the use of the R package can be found in Stroud et al.[2] Full details regarding the methods used for data collection and calculations will be available in a data paper published at a later date. References: [1] Stroud, E., Charles, M., Hodgson, J., Jones, G., and Bogaard, A. (2023). WeedEco: Classification of unknown cases using linear discriminant analysis to understand farming regimes. R package version 1.0.0, https://github.com/WeedEco/WeedEco [2] Stroud, E., Charles, M., Hodgson, J., Jones, G., and Bogaard, A. (accepted) See the fields through the weeds: introducing the WeedEco R package for comparing past and present arable farming systems using functional weed ecology. Vegetation History and Archaeobotany.
DOI: 10.5066/f7zw1j1q
title: Hydrogeologic framework bottom elevations and thicknesses of Permian, Upper Pennsylvanian, Lower Pennsylvanian, and Mississippian units in the Appalachian Plateaus Physiographic Province

description: As part of the U.S. Geological Survey Water Availability and Use Science Program study of Appalachian Plateaus aquifers, bottom elevations and thicknesses were determined for Permian, Upper Pennsylvanian, Lower Pennsylvanian, and Mississippian hydrogeologic framework units in the Appalachian Plateaus, covering parts of Pennsylvania, Maryland, Ohio, West Virginia, Kentucky, Virginia, Tennessee, Georgia, and Alabama. Thickness values for these units were compiled at point locations from available sources (Martens, 1945; McKee and Crosby, 1975; Craig and Connor, 1979; Ryder and others, 2008, 2009, 2012, 2015) and used to interpolate surfaces representing thicknesses, contact elevations, and outcrop elevations using earthVision software (Dynamic Graphics, Inc., 2014). Unit contact and outcrop elevations at cell centers from the earthVision rasters were used as the primary source to create geodatabase rasters representing bottom elevations and thicknesses of the Permian, Upper Pennsylvanian, Lower Pennsylvanian, and Mississippian framework units.
DOI: 10.5066/f7x06544
title: Mean-annual and mean-seasonal water-budget estimates from a Soil-Water-Balance model of the Appalachian Plateaus, 1980 through 2011

description: As part of the U.S. Geological Survey Groundwater Resources Program study of Appalachian Plateaus aquifers, mean-annual and mean-seasonal water-budget estimates for the period 1980 through 2011 were determined for a 162,000 square-mile area covering parts of New York, Pennsylvania, Maryland, Ohio, West Virginia, Kentucky, Virginia, Tennessee, North Carolina, Georgia, Alabama, and Mississippi. Mean-annual and mean-seasonal precipitation, recharge, and actual evapotranspiration (ET) estimates were derived from annual and monthly Soil-Water-Balance (SWB) model (McCoy and others, 2015; Westenbroek and others, 2010) output and compiled in a geodatabase. Precipitation estimates from the Appalachian Plateaus SWB model were derived from daily Daymet climate grids (Thornton and others, 2012). Estimates of recharge from the SWB model were calculated using a modified Thornthwaite-Mather soil-water accounting method (Thornthwaite and Mather, 1957; Westenbroek and others, 2010). Estimates of ET from the SWB model were derived by adjusting a spatially-variable estimate of potential ET (Hargreaves and Samani, 1985) with estimates of precipitation and soil-moisture (Westenbrok and others, 2010). The geodatabase contains polygon and point feature classes representing the model grid cells and their centers, respectively, and two tables containing mean-annual and mean-seasonal estimates for each cell. Mean-annual estimates were computed for full calendar years (January through December) and are presented in inches per year (in/yr) for the 1980 through 2011 period. Mean-seasonal estimates for spring (March through May), summer (June through August) and fall (September through November) are presented in inches for the 1980 through 2011 period. Mean-seasonal estimates for winter (December through February), also presented in inches, were calculated for December 1980 through February 2011.
DOI: 10.5066/f7p84918
title: Groundwater discharge area for Diamond Valley, Central Nevada, 1968

description: This dataset represents &quot;phreatophyte areas&quot; mapped as part of an analysis of irrigation pumping in Diamond Valley, Nevada published in 1968. The data were digitized for comparison with mapping of groundwater discharge areas done in 2011 as part of a groundwater study in these valleys published in 2016.
DOI: 10.5066/f75b00k7
title: Groundwater Discharge Area for the Diamond Valley Flow System, Central Nevada

description: This dataset represents the groundwater discharge area in the Diamond Valley Flow System, central Nevada. Vegetated areas within the groundwater discharge area are composed of phreatophytic shrubs with smaller areas of grassland, marshland, xeric vegetation, bare soil, and agricultural lands where phreatophytic shrubs historically were present. Vegetated areas outside the groundwater discharge area are primarily composed of xeric vegetation and bare soil although very sparse phreatophytic shrubs may be present on the outer margins of the boundary. The groundwater discharge area was mapped in the summer of 2011 using field reconnaissance and supporting digital data. Additional supporting field information was gathered in fall of 2014.