Pooling individual samples prior to DNA extraction can mitigate the cost of DNA extraction and genotyping; however, these methods need to accurately generate equal representation of individuals within pools. This data set was generated to determine accuracy of pool construction based on white blood cell counts compared to two common DNA quantification methods. Fifty individual bovine blood samples were collected, and then pooled with all individuals represented in each pool. Pools were constructed with the target of equal representation of each individual animal based on number of white blood cells, spectrophotometric readings, spectrofluorometric readings and whole blood volume with 9 pools per method and a total of 36 pools. Pools and individual samples that comprised the pools were genotyped using a commercially available genotyping array. ASReml was used to estimate variance components for individual animal contribution to pools. The correlation between animal contributions between two pools was estimated using bivariate analysis with starting values set to the result of a univariate analysis. The dataset includes: 1) pooling allele frequencies (PAF) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); PAF = X/(X+Y). 2) Genotypes or number of copies of B(green) allele (0,1,2). 3) Definitions for each sample. Resources in this dataset:Resource Title: Pooling Allele Frequencies (paf) for all pools and individual animals. File Name: pafAnimal.csv.gzResource Description: Pooling Allele Frequencies (paf) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); paf = X / (X + Y)Resource Title: Genotypes for individuals within pools. File Name: g.csv.gzResource Description: Genotypes (number of copies of the B (green) allele (0,1,2)) for individual bovine animals within pools.Resource Title: Sample Definitions . File Name: XY Data Key.xlsxResource Description: Definitions for each sample (both pools and individual animals).
The origin of the risk characterises the real-world entity which, through its presence, represents a potential risk. This origin may be characterised by a name and, in some cases, a geographical object locating the actual entity causing the risk. The location of the entity and the knowledge of the dangerous phenomenon are used to define the risk pools, the risk-exposed areas that underpin the RPP. The origin of the risk characterises the real-world entity which, through its presence, represents a potential risk. This origin may be characterised by a name and, in some cases, a geographical object locating the actual entity causing the risk. The location of the entity and the knowledge of the dangerous phenomenon are used to define the risk pools, the risk-exposed areas that underpin the RPP. The origin of the risk characterises the real-world entity which, through its presence, represents a potential risk. This origin may be characterised by a name and, in some cases, a geographical object locating the actual entity causing the risk. The location of the entity and the knowledge of the dangerous phenomenon are used to define the risk pools, the risk-exposed areas that underpin the RPP.
This dataset consists of 119,494 lines of data consisting of idle well fluid level depth, auxiliary measurements, and well parameters from California oil and gas wells that were reported to the California Department of Conservation, Geologic Energy Management Division (CalGEM). The dataset was provided by CalGEM in March 2018 and includes measurements made from 1976 to 2018. There are 5 sets of operator-reported data: idle well fluid level depth (N=101,734), well clean out depth (N=8,402), depth of base of fresh water (N=108,216), well top perforation depth (N=93,569), and depth reached (N=15,756). These are associated with a well, defined by API number, well number, operator name, test date, township, section, range, and pool code. While detailed metadata for these measurements was not provided by CalGEM, they are thought to be collected under idle well testing regulations. Present regulations broadly define an idle well as one that has not been used for production or injection for 24 months or longer (California Code of Regulations, 2022, Title 14 §1760). Below, a summary of current regulations related to this program are presented; however, regulations at the time of data collection may be different. Once a well is classified as an idle well, a fluid level test using acoustical, mechanical, or other methods must be conducted within 24 months, and every 24 months beyond that, as long as a well is idle, unless the wellbore does not penetrate an underground source of drinking water (USDW) (California Code of Regulations, 2022, Title 14 §1772.1). Currently, within 8 years of a well becoming idle a clean out tag is required. This is done to demonstrate that the well can be properly plugged and abandoned. A clean out tag is done by passing open-ended tubing or a gauge ring of a minimum diameter equal to that of tubing necessary to plug and abandon a well (California Code of Regulations, 2022, Title 14 §1772.1). This testing must generally be repeated once every 48 months as long as a well is classified as an idle well. Freshwater is defined as water that contains 3,000 milligrams/liter (mg/L) or less of total dissolved solids (California Code of Regulations, 2022, Title 14 §1720.1). The base of freshwater is the depth in a well where the overlying water is freshwater. Neither top perforation depth or depth reached is defined by statute. Top perforation is generally the shallowest active perforated interval. It is not clear what depth reached represents. Well elevation and pool name were added from other datasets to aid in analysis. Pools, identified by pool code and pool name, are defined as independent hydrocarbon zones (California Public Resources Code § 3227.6.b). The accuracy of the values reported to CalGEM by oil-field operators is unknown. Unrealistic values were discarded from the data as noted in the process steps. This dataset was compiled and analyzed as part of the California State Water Resources Control Board Oil and Gas Regional Monitoring Program and the U.S. Geological Survey California Oil, Gas, and Groundwater (COGG) program.
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File Name: pafAnimal.csv.gzResource Description: Pooling Allele Frequencies (paf) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); paf = X / (X + Y)Resource Title: Genotypes for individuals within pools. File Name: g.csv.gzResource Description: Genotypes (number of copies of the B (green) allele (0,1,2)) for individual bovine animals within pools.Resource Title: Sample Definitions . File Name: XY Data Key.xlsxResource Description: Definitions for each sample (both pools and individual animals).
The concept of employment pool is often used generically to define the area of influence of a particular economic pole. It corresponds to a thinner division of employment areas. Sometimes an employment pool corresponds exactly to an area of employment.The exact methodology of determination is not communicated by INSEE.The Melchior site gives this definition: INSEE has defined employment areas, but the concept of employment pool (perimeter used by the Ministry of Labour) does not have a clear definition. Basins are subdivisions of employment areas and may constitute local policy frameworks for public authorities.
KeysContains information about which data archives go with which experiments. Download this archive first, untar, and read the ReadMe.txt file within.A020513Data archiveA071014Data archiveA090613Data archiveA120713Data archiveA160413Data archiveA190413Data archiveA190913Data archiveA200613Data archiveA220713Data archiveA230713Data archiveA230913Data archiveA250413Data archiveA250613Data archiveA260413Data archiveA290413Data archiveA301014Data archiveA300413Data archiveA311014Data archiveB071014Data archiveB080613Data archiveB081014Data archiveB160713Data archiveB190913Data archiveB230913Data archiveB250613Data archiveB311014Data archiveC071014Data archiveC160713Data archiveC250613Data archiveD180413Data archive
This is an integration of 10 independent multi-country, multi-region, multi-cultural social surveys fielded by Gallup International between 2000 and 2013. The integrated data file contains responses from 535,159 adults living in 103 countries. In total, the harmonization project combined 571 social surveys.
These data have value in a number of longitudinal multi-country, multi-regional, and multi-cultural (L3M) research designs. Understood as independent, though non-random, L3M samples containing a number of multiple indicator ASQ (ask same questions) and ADQ (ask different questions) measures of human development, the environment, international relations, gender equality, security, international organizations, and democracy, to name a few [see full list below].
The data can be used for exploratory and descriptive analysis, with greatest utility at low levels of resolution (e.g. nation-states, supranational groupings). Level of resolution in analysis of these data should be sufficiently low to approximate confidence intervals.
These data can be used for teaching 3M methods, including data harmonization in L3M, 3M research design, survey design, 3M measurement invariance, analysis, and visualization, and reporting. Opportunities to teach about para data, meta data, and data management in L3M designs.
The country units are an unbalanced panel derived from non-probability samples of countries and respondents> Panels (countries) have left and right censorship and are thusly unbalanced. This design limitation can be overcome to the extent that VOTP panels are harmonized with public measurements from other 3M surveys to establish balance in terms of panels and occasions of measurement. Should L3M harmonization occur, these data can be assigned confidence weights to reflect the amount of error in these surveys.
Pooled public opinion surveys (country means), when combine with higher quality country measurements of the same concepts (ASQ, ADQ), can be leveraged to increase the statistical power of pooled publics opinion research designs (multiple L3M datasets)…that is, in studies of public, rather than personal, beliefs.
The Gallup Voice of the People survey data are based on uncertain sampling methods based on underspecified methods. Country sampling is non-random. The sampling method appears be primarily probability and quota sampling, with occasional oversample of urban populations in difficult to survey populations. The sampling units (countries and individuals) are poorly defined, suggesting these data have more value in research designs calling for independent samples replication and repeated-measures frameworks.
**The Voice of the People Survey Series is WIN/Gallup International Association's End of Year survey and is a global study that collects the public's view on the challenges that the world faces today. Ongoing since 1977, the purpose of WIN/Gallup International's End of Year survey is to provide a platform for respondents to speak out concerning government and corporate policies. The Voice of the People, End of Year Surveys for 2012, fielded June 2012 to February 2013, were conducted in 56 countries to solicit public opinion on social and political issues. Respondents were asked whether their country was governed by the will of the people, as well as their attitudes about their society. Additional questions addressed respondents' living conditions and feelings of safety around their living area, as well as personal happiness. Respondents' opinions were also gathered in relation to business development and their views on the effectiveness of the World Health Organization. Respondents were also surveyed on ownership and use of mobile devices. Demographic information includes sex, age, income, education level, employment status, and type of living area.
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Data Description: This data set contains all inspections issued/performed by City of Cincinnati Departments (including Buildings & Inspections; Cincinnati Fire Department; Cincinnati Health Department; Cincinnati Parks; and Trade/Development), as well as Inspections Bureau Inc (IBI) and Hamilton County departments.
Inspections range from electrical surveys, to swimming pools/spas, to elevator inspections, daycare inspections, and more. This data covers inspections since 1999 through present day.
Data Creation: All data is input by respective agencies, and maintained/stored by Cincinnati Area Geographic Information Systems (CAGIS), and is additionally available on CAGIS Property Activity Report website: http://cagismaps.hamilton-co.org/PropertyActivity/cagisreport
Data Created By: CAGIS
Refresh Frequency: Daily
Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.
Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).
Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad
https://data.syr.gov/pages/termsofusehttps://data.syr.gov/pages/termsofuse
Information about pools owned and maintained by the City of Syracuse. Dataset also contains information about the pools, whether they are handicap accessible, width and length of the pools, depth, and whether they have been converted to be a salt water pool.Data Dictionary:LabelDefinitionDefinition SourceParkThe name of the CIty Park.Parks and Rec. DepartmentPoolWhether there is a pool at this park. This will be a "Yes" or "No" value.Parks and Rec. DepartmentTypeThe type of pool, whether this is an Outdoor Pool, Indoor, or and Outdoor "L" shaped pool.Parks and Rec. DepartmentLatitudeThe latitude where the pool is located. This can be used with GIS mapping.Parks and Rec. DepartmentLongitudeThe longitude where the pool is located. This can be used with GIS mapping.Parks and Rec. DepartmentAccessible_PoolWhether the pool is one that has been deemed to be an Americans with Disabilities Act (ADA) accessible pool. This can be through a ramp, lift, or other means.Parks and Rec. DepartmentLength_x_WidthThe length and width of the pool. These may be measured in yards (yds), meters (M), or feet ('). Parks and Rec. DepartmentDepthThe depth of the pool measured in feet and inches.Parks and Rec. DepartmentPool_ImageWebsite link to an image of this pool. This can be used for a pop-up on a GIS map.Parks and Rec. DepartmentWebsiteThe City of Syracuse's website that contains up to date information about pool hours and other information.Parks and Rec. DepartmentDataset Contact Information:Organization: Parks and Rec DepartmentPosition:Data Program ManagerCity:Syracuse, NYE-Mail Address:opendata@syrgov.net
The Multi-Resolution Land Characteristics (MRLC) project was established to provide multi-resolution land cover data of the conterminous United States from local to regional scales. A major component of MRLC is an objective to develop a national 30-meter land cover characteristics data base using Landsat thematic mapper (TM) data. This is a cooperative effort among six programs within four U.S. Government agencies, including the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program; the U.S. Geological Survey's (USGS) National Water Quality Assessment Program; the National Biological Service's Gap Analysis Program; the USGS' Earth Resources Observation Systems (EROS) Center; the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; and the EPA's North American Landscape Characterization project. Multitemporal scenes were selected for the eastern deciduous forests, agricultural regions, and selected other regions. Multitemporal pairs were selected to be in consecutive seasons (in 1992 when possible). All scenes were previewed for image quality. The participating agencies organized the joint purchase of a single national set of Landsat TM scenes. In addition, the cooperators developed a common definition for preprocessing the satellite data. The shared, consistently processed TM data are the foundation for the development of the national 30-meter land cover data base. The jointly acquired data are archived and distributed by EROS. A variety of products are available to MRLC participants, to their affiliated users, and to the general public. Multi-Resolution Land Characterization 2001 (MRLC 2001) At-Sensor Reflectance Dataset is a second-generation federal consortium to create an updated pool of nation-wide Landsat imagery, and derive a second-generation National Land Cover Database (NLCD 2001). The MRLC 2001 data cover the United States, including Alaska and Hawaii. Multi-temporal scenes may also be available, depending on the location. Most of the images are of high quality, and cloud cover is generally less than ten percent. The data will also include a 30-meter Digital Elevation Model (DEM) for all scenes that do not include the Canadian or Mexican borders.
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This dataset comes from the Annual Community Survey questions related to resident satisfaction with City Parks, Recreation, Libraries, Arts, & Cultural Centers. Survey participants are asked : "Please rate your level of satisfaction with a) Quality of City swimming pools; b) Quality of neighborhood parks; c) Quality of City recreation & community centers; d) Quality of Tempe History Museum; e) Quality of Tempe Public Library; f) Quality of Tempe Center for the Arts." Survey respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (responses of "don't know" are excluded).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the City Parks, Recreation, Libraries, Arts, & Cultural Centers performance measure.The performance measure dashboard is available at 3.16 Community Services Facilities and Open Spaces.Additional InformationSource: Community Attitude Survey (Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: Excel and PDF ReportPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: ManualData Dictionary
This dataset is comprised of eight files related to salt marsh monitoring data or measures of of human disturbance (i.e. human impacts in terms of physical, chemical, and land-use stressors) collected at 33 marsh study units (MSUs) in five National Parks within the NPS Northeast Coastal and Barrier Network (NCBN) along the northeastern coast of the US. Two files contain data related to the species and coverage of salt marsh vegetation observed in MSUs (1 data file, 1 definitions file). Two files contain data related to the species and abundance of nekton collected from creeks, pools and ditches in MSUs (1 data file, 1 definitions file). Two files contain data related to the height of key salt marsh vegetation species observed in MSUs (1 data file, 1 definitions file). Two files contain data related to metrics describing the degree of human disturbance in MSUs (1 data file, 1 definitions file). Salt marsh monitoring data were generally collected from 2008-2013; however, salt marsh monitoring data were collected irregularly between 1997 and 2007 as part of a pilot program in a small number of the MSUs. Human disturbance metrics were derived from existing aerial imagery and the 2006 National Land Cover Database.
This produced dataset includes spatially aggregated records of measurements and observations from public and private organizations across the Upper Missouri River Basin. For this dataset the Upper Missouri River Basin is defined as Hydrologic Unit Code 1002-1013, and includes portions of the states of Montana, Wyoming, North Dakota, and South Dakota. Streamflow observations, defined as this dataset as the identification of flowing, dry, or pooled streamflow conditions, are an essential part of understanding the relationship between streamflow permanence and climatic and physical factors. For the purpose of this investigation, all streamflow observations were identified as perennial, non-perennial, or pooled to be used in the PROSPER (PRObability of Streamflow PERmanence) model.
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Urinary expressed prostatic secretion or “EPS-urine” is proximal tissue fluid that is collected after a digital rectal exam (DRE). EPS-urine is a rich source of prostate-derived proteins that can be used for biomarker discovery for prostate cancer (PCa) and other prostatic diseases. We previously conducted a comprehensive proteome analysis of direct expressed prostatic secretions (EPS). In the current study, we defined the proteome of EPS-urine employing Multidimensional Protein Identification Technology (MudPIT) and providing a comprehensive catalogue of this body fluid for future biomarker studies. We identified 1022 unique proteins in a heterogeneous cohort of 11 EPS-urines derived from biopsy negative noncancer diagnoses with some benign prostatic diseases (BPH) and low-grade PCa, representative of secreted prostate and immune system-derived proteins in a urine background. We further applied MudPIT-based proteomics to generate and compare the differential proteome from a subset of pooled urines (pre-DRE) and EPS-urines (post-DRE) from noncancer and PCa patients. The direct proteomic comparison of these highly controlled patient sample pools enabled us to define a list of prostate-enriched proteins detectable in EPS-urine and distinguishable from a complex urine protein background. A combinatorial analysis of both proteomics data sets and systematic integration with publicly available proteomics data of related body fluids, human tissue transcriptomic data, and immunohistochemistry images from the Human Protein Atlas database allowed us to demarcate a robust panel of 49 prostate-derived proteins in EPS-urine. Finally, we validated the expression of seven of these proteins using Western blotting, supporting the likelihood that they originate from the prostate. The definition of these prostatic proteins in EPS-urine samples provides a reference for future investigations for prostatic-disease biomarker studies.
The concept of a pool of employment is often used generically to define the area of influence of a particular economic cluster. It corresponds to a finer division of employment areas. Sometimes, an employment pool corresponds exactly to an area of employment.The exact determination methodology is not communicated by INSEE.The Melchior site gives this definition: INSEE has defined areas of employment, but the concept of employment basin (perimeter used by the Ministry of Labour) does not have a clear definition. Basins are subdivisions of employment areas and may constitute local policy frameworks for public authorities.
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Previous publications:
Konkoly, K. R., Appel, K., Chabani, E., Mangiaruga, A., Gott, J., Mallett, R., ... & Paller, K. A. (2021). Real-time dialogue between experimenters and dreamers during REM sleep. Current Biology, 31(7), 1417-1427.
Correspondence:
karenkonkoly2023@u.northwestern.edu
The time of awakening column contains only approximated times based on experimenters' notes and the duration of files
There are port codes in the data that have slightly meanings for some different participants (in the "status" channel). Here is a guide for their meanings:
Cases 01-08
Cases 09-33
N/A
Methods:
Twenty-two participants (15 female, age range 18-33 years, M = 21.1 ± 4.3 years) who claimed to remember at least one dream per week were recruited by word of mouth, online forum, and the Northwestern University Psychology Department participant pool. They each participated in one or more nap sessions, which amounted to 27 nap sessions in total.
Procedure:
Participants visited the laboratory at Northwestern University at approximately their normal wake time and received guidance on identifying lucid dreams and instructions for the experiment for about 40 min during preparations for polysomnographic recordings, including EEG, EMG, and EOG, using a Neuroscan SynAmps system. Participants were instructed to signal with a prearranged number of LR eye movements if they became lucid in a dream.
Next, participants practiced making ocular signals and responding to questions using combinations of LR signals. Subsequently, participants completed the Targeted Lucidity Reactivation (TLR) procedure while lying in bed. This procedure was derived from the procedure developed by Carr and collegues. A method of reality checking to induce lucid dreaming was paired with sensory stimulation and accelerated in a single session immediately before sleep, and then cues were presented again during REM sleep. In this procedure, participants were trained to associate a novel cue sound with a lucid state of mind during wake. The sound consisted of three pure-tone beeps increasing in pitch (400, 600, and 800 Hz) at approximately 40-45 dB SPL and lasting approximately 650 ms. For one participant (ppt. 121) the pure-tone beeps had previously been associated with a different task in an unrelated study. Thus, for this participant, a 1000-ms violin sound and low-intensity flashing-red LED lights were used as cues. All participants were informed that this cue would be given during sleep to help promote a lucid dream. Over the next 15 min, the TLR sound was played up to 15 times. The first 4 times, it was followed by verbal guidance to enter a lucid state as follows. ‘‘As you notice the signal, you become lucid. Bring your attention to your thoughts and notice where your mind has wandered.[pause] Now observe your body, sensations, and feelings.[pause] Observe your breathing. [pause] Remain lucid, critically aware, and notice how aspects of this experience are in any way different from your normal waking experience.’’
Participants often fell asleep before all 15 TLR cue presentations were completed. Standard polysomnographic methods were used to determine sleep state. Once participants entered REM sleep, TLR cues were presented again, at about 30-s intervals, as long as REM sleep remained stable. After participants responded to a cue with a lucid eye signal, or after approximately 10 cues were presented without response, we began the math problem portion of the experiment.
We devised the following task to engage auditory perception of math problems, working memory, and the ability to express the correct answer. We used simple addition and subtraction problems that could each be answered by a number between 1 and 4 (LR = 1, LRLR = 2, LRLRLR = 3, LRLRLRLR = 4), or between 1 and 6 for the first 5 participants.
From the above dataset, data was included in DREAM if there was a period of sleep on the EEG followed by a report of a dream (or a lack of dream). The EEG data includes the last period of continuous sleep before the dream report was collected, starting with the first epoch scored as wake, and ending at the last second before clear movement/alpha activity indicating wake. Also, there are a few instances, noted in the “Remarks” column in the “Records” file, where I included epochs that were scored as wake, when the wake seemed due to alpha from participants attempting to answer questions with eye movements (only one of these included wake in the last 20 seconds of the recording, case21_sub111).
EEG sleep data was NOT included if it was not followed by a verbal/written dream report or clear note on the experimenter’s log that there was no recall. Also not included is data where participants completed eye signals or answered questions, but it was not part of the continuous period of sleep before a dream report was given. Also excluded was a case in which a dream report was collected at the end of the nap but the participant had been in and out of sleep beforehand, so it was unclear which sleep period the report referred to.
Karen Konkoly rated reports according to the DREAM categorization. If the participant reported remembering any sort of mental content from sleep, it was rated “2”. If the participant reported remembering a dream but none of its content, it was rated “1”. If the participant reported not remembering anything, or not falling asleep, it was rated “0”.
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Regional water strategies (RWS) set the direction for water planning and management at a regional scale over the next 20-40 years. There are 14 regional water strategies (including Greater Sydney Water Strategy), tailored to the unique challenges and needs of each region. They have been developed in partnership with water service providers, local councils, communities, Aboriginal people and other stakeholders across NSW.\r \r The boundaries of regional water strategy areas define regions in NSW for which regional water strategies are prepared. The boundaries are based on several factors, including:\r \r - surface water hydrology\r - statutory instruments, such as water sharing plans and water resource plans\r - economic, social and cultural factors\r - government strategic plans\r \r The boundaries of regional water strategy (RWS) areas mostly, but not exclusively, align with groups of water sharing plan boundaries for surface water sources:\r \r - In coastal areas, RWS boundaries align with Water Sharing Plan boundaries\r - In inland areas, RWS boundaries align with Water Resource Plan boundaries. \r \r The NSW Murray RWS also includes the area for the Wentworth weir pool. Its boundary is further defined using the Bureau of Meteorology’s (BoM) geofabric AHGF Catchment layer to include the catchments that incorporate the Wentworth Weir pool. The Fish River–Wywandy RWS boundary was also further defined using the BoM geofabric, local council boundaries and National Parks Estate boundaries.\r \r -----------------------------------\r \r Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.\r
This dataset represents the dam density and storage volumes within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Anthropogenic Barrier Dataset (NABD). Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. The main objective of this project was to develop a dataset of large, anthropogenic barriers that are spatially linked to the National Hydrography Dataset Plus Version 1 (NHDPlusV1) for the conterminous U.S. to facilitate GIS analyses based on the NHDPlusV1/NHD and NID datasets. To meet this objective, Michigan State University conducted a spatial linkage of the point dataset of the 2009 National Inventory of Dams (NID) created by the U.S. Army Corps of Engineers (USACE) to the NHDPlusV1/NHD. The pool of dam data included were modified based on 1) dam removals that occurred after development of the 2009 NID and 2) the identification of duplicate dam records along state boundaries (cases where more than one state reported the same dam). The US Geological Survey (USGS) Aquatic GAP Program supported this work. The (dams/catchment) and (dam_storage/catchment) were summarized and accumulated into watersheds to produce local catchment-level and watershed-level metrics as a point data type.
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The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers and genomic prediction of single-crosses. For that, we used a method to assemble a reference genome to perform the SNP calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (Array and GBS). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population's intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.
Researchers sequenced 10,368 expressed sequence tags (EST) clones using a normalized cDNA library made from pooled samples of the trophont, tomont, and theront life-cycle stages, and generated 9,769 sequences (94.2% success rate). Post-sequencing processing led to 8,432 high quality sequences. Clustering analysis of these ESTs allowed identification of 4,706 unique sequences containing 976 contigs and 3,730 singletons. The ciliate protozoan Ichthyophthirius multifiliis (Ich) is an important parasite of freshwater fish that causes 'white spot disease' leading to significant losses. A genomic resource for large-scale studies of this parasite has been lacking. To study gene expression involved in Ich pathogenesis and virulence, our goal was to generate ESTs for the development of a powerful microarray platform for the analysis of global gene expression in this species. Here, we initiated a project to sequence and analyze over 10,000 ESTs. Resources in this dataset:Resource Title: Data Dictionary - Supplemental Tables 1, 2, and 3. File Name: IchthyophthiriusESTs_DataDictionary.csvResource Description: Machine-readable comma-separated values (CSV) definitions for data elements of Supplemental Tables 1-3 concerning I. multifiliis unique EST sequences, BLAST searches of the Ich ESTs against Tetrahymena thermophila and Plasmodium falciparum genomes, and gene ontology (GO) profile.Resource Title: Table 3. Table of gene ontology (GO) profiles.. File Name: 12864_2006_889_MOESM3_ESM.xlsResource Description: Supplemental Table 3, Excel spreadsheet; Table of gene ontology (GO) profiles; Provided information includes unique EST name, accession numbers, BLASTX top hit, GO identification numbers and enzyme commission (EC) numbers. Data resources found on the main article page under the "Electronic supplementary material" section: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-8-176 Direct download for this resource: https://static-content.springer.com/esm/art:10.1186/1471-2164-8-176/MediaObjects/12864_2006_889_MOESM3_ESM.xls Title: Table I. Multifiliis unique EST sequences. File Name: 12864_2006_889_MOESM1_ESM.xlsResource Description: Supplemental Table 1 for article, "Generation and analysis of expressed sequence tags from the ciliate protozoan parasite Ichthyophthirius multifiliis." Excel spreadsheet; Table of I. multifiliis unique EST sequences; Provided information includes I. multifiliis BLASTX top hits to the non-redundant database in GenBank with unique EST name and accession numbers. Also included are significant protein domain comparisons to the Swiss-Prot database. Putative secretory proteins are highlighted. Data resources found on the main article page under the "Electronic supplementary material" section: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-8-176 Direct download for this resource: https://static-content.springer.com/esm/art:10.1186/1471-2164-8-176/MediaObjects/12864_2006_889_MOESM1_ESM.xls Title: Table 2. Excel spreadsheet; Summary of BLAST searches of the Ich ESTs against Tetrahymena thermophila and Plasmodium falciparum genomes. File Name: 12864_2006_889_MOESM2_ESM.xlsResource Description: Table 2 from "Generation and analysis of expressed sequence tags from the ciliate protozoan parasite Ichthyophthirius multifiliis." Excel spreadsheet; Summary of BLAST searches of the Ich ESTs against Tetrahymena thermophila and Plasmodium falciparum genomes. Provided information includes I. multifiliis BLASTX top hits to the non-redundant database in GenBank with unique EST name, tBLASTx top hits to the T. thermophila genome, and BLASTX top hits to the P. falciparum genome sequences. This table correlates with the Venn diagram in figure 1. Data resources found on the main article page under the "Electronic supplementary material" section: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-8-176 Direct download link for this data resource: https://static-content.springer.com/esm/art:10.1186/1471-2164-8-176/MediaObjects/12864_2006_889_MOESM2_ESM.xls
Pooling individual samples prior to DNA extraction can mitigate the cost of DNA extraction and genotyping; however, these methods need to accurately generate equal representation of individuals within pools. This data set was generated to determine accuracy of pool construction based on white blood cell counts compared to two common DNA quantification methods. Fifty individual bovine blood samples were collected, and then pooled with all individuals represented in each pool. Pools were constructed with the target of equal representation of each individual animal based on number of white blood cells, spectrophotometric readings, spectrofluorometric readings and whole blood volume with 9 pools per method and a total of 36 pools. Pools and individual samples that comprised the pools were genotyped using a commercially available genotyping array. ASReml was used to estimate variance components for individual animal contribution to pools. The correlation between animal contributions between two pools was estimated using bivariate analysis with starting values set to the result of a univariate analysis. The dataset includes: 1) pooling allele frequencies (PAF) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); PAF = X/(X+Y). 2) Genotypes or number of copies of B(green) allele (0,1,2). 3) Definitions for each sample. Resources in this dataset:Resource Title: Pooling Allele Frequencies (paf) for all pools and individual animals. File Name: pafAnimal.csv.gzResource Description: Pooling Allele Frequencies (paf) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); paf = X / (X + Y)Resource Title: Genotypes for individuals within pools. File Name: g.csv.gzResource Description: Genotypes (number of copies of the B (green) allele (0,1,2)) for individual bovine animals within pools.Resource Title: Sample Definitions . File Name: XY Data Key.xlsxResource Description: Definitions for each sample (both pools and individual animals).