6974 research outputs found
Sort by
Accounting for the Sustainable Development Goals:Walking the talk or managing impressions?
No full textPurpose – This paper examines the extent, quality, and tone of Sustainable Development Goals (SDG) disclosures by UK FTSE 100 firms, assessing whether these reflect genuine commitment or serve impression management purposes.Design/Methodology – A meaning-oriented content analysis of SDG-related disclosures from 75 FTSE 100 company reports is conducted. Drawing on the SDG Compass and KPMG’s SDG reporting criteria, we develop a multidimensional analytical framework to assess disclosure scope, quality (semantic attributes and managerial orientation), and tone (thematic manipulation and structural emphasis).Findings – SDG reporting is widespread but predominantly symbolic. Most firms emphasise a narrow set of high-profile goals (notably SDGs 8, 12, 13), with disclosures often framed in overly optimistic language, reinforcing favourable corporate narratives. Crucially, our targeted analysis reveals that many SDG sub-targets are poorly aligned with existing sustainability reporting frameworks, limiting their operational relevance. We demonstrate that these structural deficiencies, coupled with managerial apathy, foster superficial engagement and constrain meaningful accountability.Originality/value – This study reframes symbolic SDG engagement as the outcome of both managerial choices and structural limitations within the SDG architecture. It contributes to SDG disclosure literature by developing a composite framework that integrates disclosure scope, quality, and narrative tone—offering a multidimensional lens for evaluating sustainability reporting. By extending the analytical lens beyond headline goals, it offers fresh insights into the institutional and managerial conditions that legitimise symbolic reporting. These insights are particularly salient as deliberations on the post-2030 sustainable development agenda gather momentum.Practical implications – The paper calls for the development of more specific, business-relevant SDG sub-targets and performance indicators, enhanced assurance practices, and stronger alignment of post-2030 frameworks with established sustainability reporting standards
Genomic evidence for hybridization, mitochondrial capture and cryptic invasions in the Chinese mitten crab
No full textThe following datasets and coding files are provided: Crab.all.snps.VCF: all SNP data from Genotyping by Sequencing of mitten crab DNA. Full Filtering Script.txt: script for filtering SNPs in R. DAPC Script.R: script for generating DAPC plots from SNP data dig pics2015: tps file containing morphological landmark data from 2015 sample of mitten crabs Landmark Script2015.txt: script for performing procrustes PCA on 2015 landmark data in R. dig pics2020: tps file containing morphological landmark data from 2020 sample of mitten crabs Landmark Script2020.txt: script for performing procrustes PCA on 2020 landmark data in R
PanCam Operations Toolkit (PCOT)
No full textThe Pancam Operations Toolkit is a Python application and library primarily intended for processing data from the Pancam instrument on the Rosalind Franklin rover, although it lends itself to any task involving processing multispectral image data. For example, with PCOT you can: load ENVI (BSQ interleaved 4-byte float) and (to a basic level) PDS4 multispectral images load multiple images in other formats (e.g. PNG) and combine them into multispectral images define regions of interest in the data perform mathematical operations view spectra and histograms and many other things besides. PCOT is highly extensible and open-source, so any missing functionality is easily added. PCOT operates on a graph model - the data is processed through a set of nodes which manipulate it in various ways (e.g. add regions of interest, perform maths, splice images together, merge image channels, plot spectra). A PCOT document describes this graph, and we intend that documents are distributed along with the data they generate to help reproducibility
Additional file 1 of Elevating larval source management as a key strategy for controlling malaria and other vector-borne diseases in Africa
No full textSupplementary Material 1
Additional file 2 of Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize
No full textSupplementary Material 2: Supplementary Figure S2. Farm CPU-based Manhattan and Q-Q plots of genome-wide association study (GWAS) on eight traits evaluated under optimum (_OPT) and drought (_DS) environmental conditions. The − log10(p) values on the Y-axis in the Manhattan plot represent grain yield (GY), Days to 50 % anthesis, (AD), Days to 50 % silking (SD), Anthesis-Silking Interval (ASI), Plant height (PH), Ear height (EH), Ear position (EPO) and Ear per plant (EPP) plotted against chromosome position on X-axis. The red and blue solid horizontal lines in the Manhattan plots represent the genome-wide (− log10 (p) =6.2). The quantile—quantile plots represent observed against the expected −log10 (p)
Additional file 4 of Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize
No full textSupplementary Material 4: Supplementary Table S1. Detailed information about pedigree of 236 lines and groupings based on markers used in the study. Supplementary Table S2. Summary of SNPs distribution across the ten maize chromosomes. Supplementary Table S3. Significant QTNs identified for eight traits across multi-environment trials under optimum conditions using six multi-locus GWAS models. Supplementary Table S4. Significant QTNs identified for eight traits across multi-environment trials under drought conditions using six multi-locus GWAS models. Supplementary Table S5. Significant QTNs associated with eight traits under drought and optimum condition using Farm CPU GWAS model from GAPIT. Supplementary Table S6. Number of stable QTNs detected by at least two GWAS models for grain yield, flowering traits and other agronomic traits under drought and optimum conditions
Additional file 3 of Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize
No full textSupplementary Material 3: Supplementary Figure S3. Manhattan and Q-Q plots of genome-wide association study (GWAS) on eight traits evaluated under optimum (OPT) and drought (_DS) environmental conditions for six GWAS models. The pink dots above the threshold indicates significant QTNs identified by more than one ML-GWAS models, while green and blue dots above the threshold represent significant QTNs identified by a single ML-GWAS model . The black horizontal dashed line indicates the genome-wide significance threshold, corresponding to a −log10 (p) value of a LOD score ≥ 3.0 for ML-GWAS models
Additional file 1 of Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize
No full textSupplementary Material 1: Supplementary Figure S1. Population structure analyses of 236 maize inbred lines based on 215,542 SNPs : (a) Evanno plot of the number of clusters (K) against delta K to determine the optimum number of K; (b) graphical representation of the 236 lines at K = 3 to K = 10. Each individual is shown as a vertical line divided into K colored segments, with segment lengths indicating the estimated probability of membership to each cluster
