Cast Number measured via Uncategorized in . Part of dataset Nutrients from R/V Melville MV1015 in the South Pacific from Arica, Chile to Easter Island from November to December 2010 (C-MORE project)

This dataset includes conductivity, temperature, and depth (CTD) data from casts performed in the northern Gulf of Mexico during R/V Atlantis cruise (AT18-02) from November 26 to December 02, 2010. The cruise departed from Galveston, Texas and ended in Gulfport, Mississippi.

Cast Number measured via Uncategorized in . Part of dataset Total Particulate Carbon and Nitrogen Concentration from R/V Melville MV1015 in the S. Pacific from Arica, Chile to Easter Island, 2010 (C-MORE project)

Experiment

Three healthy adult participants wore a cast covering the entire right upper extremity for two weeks. They were scanned every day for 6-9 weeks. Scans included 42-64 daily 30-minute resting-state functional MRI scans before, during and after casting. Participants later underwent 12-24 additional scans as part of a control experiment. In all, we collected 27-43 hours of resting-state functional MRI data in each individual.

Participants also performed a block-design movement task (right hand, left hand, right foot, left foot, tongue) for 8 minutes each night prior to casting.

Details of this dataset are described in Newbold et al., Plasticity and Spontaneous Activity Pulses in Disused Human Brain Circuits, Neuron (2020), https://doi.org/10.1016/j.neuron.2020.05.007. This manuscript should be cited whenever publishing work using this dataset.

Raw data

Sessions are grouped into 5 conditions. 3 conditions (pre, cast, post) correspond to the original experiment. 2 conditions (on, off) correspond to a control experiment in which participants wore a removable cast during scanning (on sessions) but were not casted during daily life.

Two participants were also studied in a previous experiment, the Midnight Scan Club (MSC) experiment (Gordon et al, 2017, https://openneuro.org/datasets/ds000224). sub-cast1 was sub-MSC02. sub-cast2 was sub-MSC06. Carried forward many of the methods from the MSC experiment to the current study.

MSC participants were scanned using a 3T Siemens Trio MRI scanner. BOLD data were acquired at a spatial resolution of 4mm, single-band, with a TR of 2.2s. We used identical sequences for sub-cast1 during the original cast experiment (but not during the later control experiment).

After running sub-cast1, a new MRI scanner became available. sub-cast2 and sub-cast3 were scanned on a 3T Siemens Prisma using new sequences. The updated scanner and sequences were also appleid to sub-cast1 during the later control experiment. BOLD data for these scans were acquired at a spatial resolution 2.4mm, multi-band 4, with a TR of 1.1s.

Pre-processed data

In addition to the raw BOLD and structural data we collected, we have also provided fully pre-processed rs-fMRI and task-fMRI data. Processing pipelines are described in Newbold et al, 2020 and all processing scripts are available on GitLab (https://gitlab.com/DosenbachGreene/cast-induced-plasticity). Processed data are provided in volume space as well as cifti space -- combined cortical surface data and sub-cortical/cerebellar volume data.

Surface projection followed methods described in Gordon et al, 2017. Derivative structural files needed for cifti creation (e.g. pial/white surfaces, subcortical masks) are provided for sub-cast2 and sub-cast3. Because sub-cast1 was scanned using the same scanner and sequences used for the MSC study, cortical projections for sub-cast1 used the projection files generated for sub-MSC02 (https://openneuro.org/datasets/ds000224, derivatives/surface_pipeline/sub-MSC02/fs_LR_Talairach/).

Parcellations

Surface parcellations for sub-cast3 were created using methods described by Gordon et al (2017). Corresponding parcellations for sub-cast1 and sub-cast2 can be found in the MSC dataset (https://openneuro.org/datasets/ds000224, derivatives/surface_pipeline/sub-{subject}/surface_parcellation).

CTD rosette casts were conducted to provide profiles of the water column and to take water samples.

These logs are in pdf format.

CTD cast sheets (PDF) with basic information regarding when/where the CTD rosette was deployed and the niskin bottles that were sampled for various measurements. Cast sheets are provided from the POWOW1 (TN277), POWOW2 (KM1301) and POWOW3 (KM1312) cruises.

R package source code implementing the CASTER algorithm. The CASTER algorithm extends the CAST algorithm (Ben-Dor et al. 1999). This R package also provides the original CAST algorithm implemented in R. CASTER and CAST are correlation clustering algorithms for clustering data given the set of pairwise similarities among elements, and a similarity threshold. Ben-Dor, A., & Yakhini, Z. (1999, April). Clustering gene expression patterns. In Proceedings of the third annual international conference on Computational molecular biology (pp. 33-42). License: GNU General Public License V3

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

IronExII Cast Log

Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated.

CTD datasets from the CGOA LTOP project

Contributor: Tom Weingartner

This dataset was generated from mined, secondary data collected during the Deepwater Horizon oil spill subsea dispersed oil monitoring. Teh secondary is publicly available from: BP Gulf Science Data (2019) Chemistry data associated with water column samples collected in the Gulf of Mexico from May 2010 through July 2012. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University-Corpus Christi. doi:10.7266/N747489X. This dataset is associated with the following publication: Conmy, R., A. Hall, D. Sundaravadivelu, B. Schaeffer, and A. Murray. Fluorescence-estimated oil concentration (Foil) in the Deepwater Horizon subsea oil plume. MARINE POLLUTION BULLETIN. Elsevier Science Ltd, New York, NY, USA, 180: 113808, (2022).

CTD Down Casts - RR1202

. Visit https://dataone.org/datasets/sha256%3A6b9d3baf5cb384597f992f0db478721015038003025b37e6cc14fc4ecde224f2 for complete metadata about this dataset.

CTD Cast Number measured via CTD in null. Part of dataset Abundance of Picophytoplankton Fixed Samples using Flow Cytometry at the Costa Rica Dome (CRD FluZie project)

CTD Cast Number measured via CTD in null. Part of dataset Abundance of Picophytoplankton Live Samples using Flow Cytometry at the Costa Rica Dome (CRD FluZie project)

The is the data set for the paper [Jørgensen, P. S., Christiansen, C. D. & Bjørk, R., Freeze-casting uniformity and domains, Journal of the European Ceramic Society, 45, 116907, 2025]. The DOI for the publication is 10.1016/j.jeurceramsoc.2024.116907This dataset contains three folders with the data shown in the article. Besides this, there is a movie showing the domain structure throughout the dataset, as mentioned in the article.The folder "Raw_reconstructions" contains the raw reconstructed TIF images of the high resolution cutout (4X) and the bottom and top scans in the respective subfolders. These are shown in Fig. 2.The folder "Segmentations" contains the segmented structure, i.e. split into pores (with a value of 0) and solid (with a value of 1) of the high resolution cutout (4X) and the bottom and top scans in the respective subfolders. The structures are stored as a 3D logical array, corresponding to the physical directions x, y and z, in a Matlab file format. These are shown in Fig. 2 in the article.The folder "Computed_properties" contains the various computed properties illustrated in the article. The files are individually described below:Porosity_4X.mat: Contains the porosity as function of the vertical (z) distance for the high resolution cutout shown in Fig. 4 in the article. The data is stored as two arrays in a Matlab file.PSD_4X.mat: Contains two variables, which are the solid particle size distribution and the pore particle size distribution, shown in Fig 3b in the article. These are two dimensional arrays that in the first column contains the particle diameter in um and in the second column contains the volume covered in percent.Structure_tensor_bottom.mat: Contains three arrays for the structure tensor for the bottom scan. The first array is the voxel_size array, which simply gives the voxel size also stated in the article. The second array is the image_stack array with dimensions x by y by color by z. There are three colors (RGB) for each image. These are the images visualized in Fig. 6 in the article. The third array is the theta_slice which contains the local orientation angle with dimensions x, y and z. The latter array has been upsampled in x and y, as also described in the article, such that it has onethird the dimensions of the image_stack in x and y.Structure_tensor_top.mat: Contains three arrays for the structure tensor for the top scan. The variables are identical to Structure_tensor_bottom.mat.Structure_tensor_domains.mat: Contains two arrays for the structure tensor combined for the top and bottom scans. The array domain_sizes_slice contains the size of the domains with the dimensions of the array being the slice number times the number of color bins, which is taken to be five, times the number of domains with a size corresponding to the number of pixels. As an example the entry domain_sizes_slice(100,1,50) contains the number of domains in color bin 1 with a size of 50 pixels in the 100 vertical slice in the data set. Similarly the array centroid_positions_slice has the same structure. Its first dimension is the slice number, its second dimension is the color bin, its third dimension is a running number for each domain present and the final dimension of two contains the x and y coordinates in pixels of the centroid position of the respective domains. To make the structure an array, there can be empty numbers for high running domain number. As an example centroid_positions_slice(100,1,:,:) contains all domains in vertical slice 100 in color bin 1. There are 365 such domains, meaning that centroid_positions_slice(100,1,366,:) and onwards contains zeros.Tau_por_dir_1_4X.mat: Contains an array with the relative local flux in the pores in the high resolution cutout in the x-direction, as visualized in Fig. 5 in the article.Tau_por_dir_2_4X.mat: Contains an array with the relative local flux in the pores in the high resolution cutout in the y-direction, as visualized in Fig. 5 in the article.Tau_por_dir_3_4X.mat: Contains an array with the relative local flux in the pores in the high resolution cutout in the z-direction, as visualized in Fig. 5 in the article.Tau_sol_dir_1_4X.mat: Contains an array with the relative local flux in the solid in the high resolution cutout in the x-direction, as visualized in Fig. 5 in the article.Tau_sol_dir_2_4X.mat: Contains an array with the relative local flux in the solid in the high resolution cutout in the y-direction, as visualized in Fig. 5 in the article.Tau_sol_dir_3_4X.mat: Contains an array with the relative local flux in the solid in the high resolution cutout in the z-direction, as visualized in Fig. 5 in the article.

Sound velocity and CTD (conductivity, temperature, depth) cast data were collected at 9 sites offshore Santa Cruz, CA during USGS field activity 2021-619-FA in April of 2021. Aboard the R/V Parke Snavely (RVPS), a SonTek CastAway-CTD was used to collect these data at in the upper 67 meters of the water column. these data is provided in csv format, a shapefile of cast locations, as well as PNG plots of the speed of sound as a function of depth for each cast location.

CTD Cast Sheets (.pdf files)
One file per CTD cast

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

The global flexible cast iron pipe market is experiencing robust growth, driven by increasing urbanization, infrastructure development projects, and the rising demand for durable and reliable piping solutions in municipal engineering, civil construction, and industrial applications. While precise market size figures for the base year (2025) are not provided, industry reports suggest a significant market value, potentially exceeding $2 billion USD, considering the substantial investments in infrastructure globally. Assuming a conservative Compound Annual Growth Rate (CAGR) of 5% based on similar material markets, the market is projected to reach approximately $2.6 billion USD by 2030 and potentially exceed $3 billion USD by 2033. This growth is fueled by several key trends, including the adoption of sustainable infrastructure practices and the increasing preference for ductile iron pipes due to their enhanced flexibility and resistance to corrosion compared to traditional rigid cast iron pipes. However, factors such as fluctuating raw material prices and potential competition from alternative piping materials like plastic pipes act as restraints to market growth. The market segmentation reveals a significant share held by the municipal engineering sector, followed by civil and industrial construction. Among pipe types, Type W and Type A likely dominate based on typical industry standards, accounting for the majority of the market share. Key players like Jay R. Smith, Mifab, ZURN, Hargreaves Drainage, Xylem, Xinxing Pipes, Yongtong Ductile Cast Iron Pipe, and SUNS are actively shaping the market landscape through innovation in materials, manufacturing techniques, and expanding their global reach. Regional analysis shows strong growth in Asia-Pacific, driven primarily by infrastructure development in countries like China and India, while North America and Europe maintain stable but potentially slower growth due to relatively mature infrastructure. This dynamic interplay between driving factors, market trends, competitive dynamics, and regional variations makes the flexible cast iron pipe market an attractive yet challenging sector to navigate.