In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the second spreadsheet of seven in the Baseline Survey for an Impact Evaluation of the Greenbelt Transformation Initiative in South Sudan-Data.

This dataset is about book subjects and is filtered where the books is Making data work : empowering and enabling data transformation, featuring 2 columns: book subject, and publication dates. The preview is ordered by number of books (descending).

Additional file 2: Table S1.

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate Direct design using computational fluid dynamics (CFD): cost-prohibitive Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this project.

Abstract Most of the commonly used fast Fourier transform subroutines can not handle large data matrices because of the restriction imposed by the system's core memory. In this paper we present a two dimensional FFT program (SW2DFFT) and its long write-up. SW2DFFT is a Fortran program capable of handling large data matrices both square and rectangular. The data matrix is stored externally in a direct access mass storage. The program uses a stepwise approach in computing the large matrices based on the... Title of program: SW2DFFT Catalogue Id: ABFB_v1_0 Nature of problem Any problem that requires Fourier Transformation of a large 2-D data matrix. Versions of this program held in the CPC repository in Mendeley Data ABFB_v1_0; SW2DFFT; 10.1016/0010-4655(89)90108-2 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Two metal−organic frameworks (MOFs), MOF-501 and MOF-502, respectively, formulated as Co2(BPTC)(H2O)5·Gx and Co2(BPTC)(H2O)(DMF)2·Gx (BPTC = 3,3‘,5,5‘-biphenyltetracarboxylate; G = guest molecules), have been synthesized and structurally characterized, and their topologies were found to be based on the NbO (MOF-501) and PtS (MOF-502) nets. Heating MOF-501 in solution results in the more thermodynamically favored MOF-502.

Include: I. Helmert transfortmation for GNSS network program (main script: pyacs_make_time_series.py) in Python3 II. Data for Helmert transformation program (pyacs_make_time_series.py): 1. free solution in WGS84: h9801031200_alp1.snx.ss 2. list sinex file: lsinex_1file.snx 3. reference solution in ITRF: IGS12P33.snx 4. discotinuity file: soln_2012.snx III. Helmert program Manuals IV. Result of test

The SQL Server Transformation Market is projected to reach a value of 1.55 billion by 2033, growing at a CAGR of 4.49% from 2025 to 2033. The growing need for data migration, data integration, and data quality management in various industries drives market growth. Additionally, the adoption of cloud-based and open-source tools for SQL Server transformation is further contributing to market expansion. The market is segmented by tool type, deployment model, database type, business function, and industry vertical. Cloud-based tools hold a dominant position in the market due to their scalability, flexibility, and cost-effectiveness. On-demand deployment models are also gaining popularity as they provide flexibility and pay-as-you-go pricing. Relational databases are widely used for SQL Server transformation, but NoSQL and in-memory databases are emerging as viable alternatives for specific applications. Data migration remains a critical business function, followed by data integration and data quality management. The healthcare, banking and financial services, and retail and e-commerce sectors are the largest end-users of SQL Server transformation solutions. The Global SQL Server Transformation Market size is estimated to grow to over a billion by 2023, witnessing a steady growth of 4.4% from 2018 to 2023. Key drivers for this market are: 1. Cloud migration Modernization 2. Data integration 3. Analytics Security. Potential restraints include: 1. Cloud adoption 2. Digital transformation initiatives 3. Data modernization.

XSLT (lyhenne sanoista Extensible Stylesheet Language Transformations) on XML-pohjainen merkintäkieli XML-tiedostojen muunnoksiin.(31.08.2011)

Data transformation of the reference decomposition rates (kref), often derived as turnover times or in alternative formats, is commonly used to develop ecological models to project the persistence of soil organic matter (SOM). However, the effects of reciprocal or logarithmic transformation of kref on model performance and edaphic-climatic patterns remain uncertain. Here, we convert published kref values into reciprocal or logarithmic formats and establish machine learning models between the transformed kref and edaphic-climatic predictors. We show that models trained with the transformed kref exhibit 11.6-68.4% reductions in model performance upon re-conversion to kref compared to those trained with the original kref. The variable importance analysis identifies distinct key predictors governing the original kref and its transformed counterparts. This suggests that data transformation alters the relative significance of predictors without necessarily improving kref prediction performances. Consequently, our study underscores the importance of directly focusing on the original values rather than alternative representations when dissecting a given variable's patterns and pertinent mechanisms in ecological modelling. Methods A global dataset of first-order kinetics parameters and corresponding explanatory predictors is arranged as an online spreadsheet with 859 records (Xiang et al., 2023). The fitted first-order kinetics parameters in the arranged dataset were obtained from literatures fitting laboratory incubation data with one pool (M1), two pool (M2), or three pool (M3) first-order models. This arranged dataset contains eleven explanatory factors, including (i) two climatic factors: MAP (mean annual precipitation, units: mm) and MAT (mean annual temperature, units: °C), which represent the characteristics of regional climate conditions; (ii) five edaphic factors: Sand (sand fraction, units: %), Clay (clay fraction, units: %), pH, SOC (soil organic carbon, unit: g kg-1), and MBC (microbial biomass carbon, units: g C m-2), which reflect the effects of soil property and microbial community; (iii) two topographic factors: Elev (elevation, units: m) and Slope (terrain slope, units: degree or °), indicating the impact of terrain; (iv) one vegetation factor characterizing the effects of vegetation coverage: NDVI (normalized difference vegetation index); and (v) one factor representing incubation condition: IncT (laboratory incubation temperature, units: °C). To comprehensively consider the effects of soil physicochemical properties, we explored five explanatory variables in addition to the eleven variables in Xiang et al. (2023), including (i) one vegetation variable reflecting the effect of plant productivity: NPP (net primary productivity, units: g C m-2); (ii) one variable representing the effect of soil physical properties: CFVO (volumetric fraction of coarse fragment, units:%); and (iii) three variables representing the effect of soil fertility: TN (soil total nitrogen, units: g kg-1), CNratio (the ratio of soil organic carbon to total nitrogen), CEC (cation exchange capacity, units: cmol kg-1). The values of the sixteen explanatory factors were obtained from literatures corresponding to each incubation experiments. For studies not providing values of the explanatory factors, we extracted from global maps pertaining to geographic location.

Many ion storage compounds used for electrodes in Li-ion batteries undergo a first order phase transformation between the Li-rich and Li-poor end-members during battery charge and discharge. This often entails large transformation strains due to lattice misfits, which may hamper charge and discharge kinetics. Iron(III) hydroxide phosphate, Fe2–y(PO4)(OH)3–3y(H2O)3y−2 is a promising new cathode material with high Li-ion storage capacity, low production costs and low toxicity. Previous reports on this material indicate that the Li-ion intercalation and extraction in this material is accompanied by a second-order solid solution transformation. However, direct information about the transformation mechanism in Fe2–y(PO4)(OH)3–3y(H2O)3y−2 is lacking, and several details remain unclear. In this work, Fe2–y(PO4)(OH)3–3y(H2O)3y−2 is prepared by hydrothermal synthesis and characterized structurally, morphologically and by electrochemical analysis (galvostatic cycling and cyclic voltammetry). A wide range of synthesis conditions is screened, which provides information about their correlation with chemical composition, crystallite size, particle morphology and electrochemical performance. The phase transformation mechanism of selected materials is investigated through synchrotron radiation powder X-ray diffraction collected during galvanostatic discharge–charge cycling. This confirms a complete solid solution transformation both during Li-insertion (discharge) and -extraction (charge), but also reveals a highly anisotropic evolution in lattice dimensions, which is linked to an irreversible reaction step and the high vacancy concentration in Fe2–y(PO4)(OH)3–3y(H2O)3y−2.

A-share listed companies of the Shanghai and Shenzhen Stock Exchanges from 2008 to 2020 were selected to comprise the research sample. Considering the particularity of the industry characteristics, the financial industry, special treatment companies, and missing data of variables were eliminated. Moreover, the final research sample contained 14,048 firm-year observations. All continuous variables were winsorized at the 1 and 99% levels. Financial and tax risk data were obtained from the China Stock Market and Accounting Research and Wind databases. The data of enterprise digital transformation were obtained using Python technology through text analysis.

A highly stable and rigid rod metal–organic framework (MOF) is obtained by the single-crystal-to-single-crystal transformation of a Zr6 cluster-based MOF with highly interconnected but geometrically mismatched building blocks. The transformation results in a significant framework contraction, which comes from the formation of infinite chains of carboxylate- and aquo-linked Zr6 clusters as a one-dimensional rod secondary building unit. The permanently microporous rod MOF is stable in a variety of solvents, including H2O, and even under very harsh conditions, such as strongly acidic and basic aqueous solutions at 100 °C.

The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1900. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Binary Grid Shift File (.GSB format) to transform coordinates between the NAD27 and NAD83 reference systems and vice-versa.

The present article deals with an one-to-one structure–property correspondence of a dinuclear iron complex, [Dipic(H2O)FeOH]2·H2O (1) (Dipic = pyridine-2,6-dicarboxylic acid). Variable-temperature X-ray single-crystal structural analysis confirms a phase transition of complex 1 to complex 2 ([Dipic(H2O)FeOH]2) at 120 °C. Further, single-crystal-to-single-crystal (SCSC) transformation was monitored by temperature-dependent single crystal X-ray diffraction, powder X-ray diffraction, time-dependent Fourier-transform infrared spectroscopy, and differential scanning calorimetry. SCSC transformation brings the change in space group of single crystal. Complex 1 crystallizes in the C2/c space group, whereas complex 2 crystallizes in the Pi̅ space group. SCSC transformation brings the change in packing diagram as well. Complex 1 shows two-dimensional network through H-bonding, whereas the packing diagram of complex 2 shows a zigzag-like arrangement. Phase transformation not only fetches structural changes but also in the magnetic properties. Difference in Fe–O–Fe bond angles of two complexes creates notable variation in their antiferromagnetic interactions with adjacent metal centers.

The Anthropogenic Biomes of the World, Version 2: 2000 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 2000. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

H2SO4-mediated one-pot stereocontrolled (4 + 2) annulation of 4-alkenols and oxygenated naphthalenes provided tetanthrenes in CH2Cl2 at 25 °C for 10 h. The use of various Brønsted acids or Lewis acids was investigated for a facile and efficient transformation. A plausible mechanism has been proposed.