African economies are facing a series of challenges to their post-pandemic recovery. Economic activity in the region is slowing to 3.3 percent amid global headwinds, including weak global growth and tightening global financial conditions. Elevated inflation rates and resulting policy tightening, as well as the rising risk of debt distress, are also impacting economic activity. While food insecurity in Sub-Saharan Africa was increasing before the onset of Covid-19, the pandemic and the food and energy crisis have contributed to the recent steep increase in food insecurity and malnutrition. Climate shocks, low productivity in agriculture, lack of infrastructure also contribute to rising food insecurity in the region. The economic fallout from the multiple crises affecting the region has lowered household incomes, increased poverty, widen inequality and heightened food insecurity. This report discusses short-term measures combined with medium- to long-term policy actions that can strengthen African countries' capacity to build resilience and seize opportunities to unlock productivity-enhancing growth while protecting the poor and vulnerable.
This report discusses the readout from World Bank Group President David Malpass’s meeting at the at the High-Level event on access to grains and fertilizers in Africa during UNGA 77. The global food, energy, and fertilizer crisis is taking a toll on developing countries. These sectors are closely interlinked. Natural gas is used both as a feedstock and energy source in the production of ammonia, accounting for 70 to 80 percent of ammonia production costs. The rapid increase in gas prices has turned into an increase in fertilizer prices, with fertilizer prices tripling over the past two years. Last Friday, we released our Food Security Update, despite the recent stabilization of agriculture prices and the resumption of grain exports from the Black Sea, high food inflation and food security remain a critical concern. The challenge is meeting the immediate demand for fertilizers to support next season’s crops. Current projections suggest that Africa’s unmet demand could reach four million metric tons this year, with West Africa facing the most acute challenges this growing season.
While most households in Latin America and the Caribbean use mobile broadband via smartphones, expensive fees and poor service quality pose major obstacles for potential users. In addition, power outages are a challenge for nearly 40 percent of existing mobile broadband users. Addressing the region’s need for faster, cheaper, and more reliable internet connections is thus a policy and investment priority. There are persistent and significant gaps in digital infrastructure between countries in the region, as well as weighty rural-urban gaps within some countries. Bridging these digital divides will be key to inclusive digital transformation. Households with tertiary education are on average more connected (with better quality service and higher expenditures on data) compared to the rest of the population. As education level is correlated with income, digital inequalities mirror and may amplify existing social inequalities – underscoring the critical need to address them. Over two-thirds of connected households in the region are concerned about privacy and security when using the internet. However, households on average across Latin America and the Caribbean still reported increasing their use of the internet amid the pandemic, suggesting that neither issue poses a barrier to their internet use at present.
As technology becomes ever integrated into our food system and everyday life, our food industry and supply become ever more vulnerable to attack. Cyber attacks continue to threaten large and small companies, government agencies, individuals, and food and agriculture. This module, ‘Securing the Food Industry,’ aims to introduce the idea of cyberbiosecurity through a lecture format along with three case studies allowing students to interact and think through the concepts and materials. This module was built for implementation into college level courses with connection or interest in the food industry, food science, and agriculture as well as and technology courses focused on real world applications. The lecture starts by introducing the amount of technology in food science and the food industry then transitions into concerns about security. After discussing multiple subtypes of security already integrated into the food industry, cyberbiosecurity is introduced. The term and definition are discussed before the categories of cyber attacks are introduced. The lecture relates these ideas back to the food industry before sharing a few real-life examples of detrimental cyber-attacks. The lecture concludes are explain the impact a cyber attack can cause, who is responsible for preventing and recovering from these attacks, as well as suggested practices to reduce vulnerabilities. Three theoretical but realistic case studies with discussion questions follow the lecture. These studies were written to act as small group discussion starters but could be used for whole class discussion, individual writing assignments, or other applications. A list of additional resources can be found with the course material. This list provides a small sampling of additional documents which discuss cyberbiosecurity. The resources listed at the end of the lecture are not included in the additional resources document but also provide helpful information in the exploration and understanding of cyberbiosecurity. Food science resources are also included in this document to provide additional background around the food industry portion of this course material. Securing the Food Industry is an open educational resource (OER). Instructors reviewing, adopting, or adapting the module should indicate their interest at https://forms.gle/orFRGhYs8owBP7gD6. Virginia Tech Center for Advanced Innovation in Agriculture (CAIA) the Commonwealth CyberInitiative Southwest Virginia node (CCI SWVA) Securing the Food Industry is an open educational resource (OER). Instructors reviewing, adopting, or adapting the module should indicate their interest at https://forms.gle/8JB3tmHrddCvD9926.
The River Sediment Database (RiverSed) database contains Total Suspended Sediment (TSS) concentrations derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. TSS concentrations represent reach integrated medians concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected ithin each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). Files: 1) Metadata (RiverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (riverSed_usa_v1.0.txt). Table of TSS concentration and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv). 6) Matchup database with extended metadata on locations and in-situ data (Aquasat_TSS_v1.0.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_v1.csv) 8) The xgboost model that can make TSS predictions over inland waters in USA with 9 Landsat bands/band combinations (final_model_xgbLinear_v1.rds). The model can be loaded in R. Future version will have the xgb object to be compatible across languages.
Women and girls play a key role in agriculture and food systems. Globally, women and girls play an important role in the agricultural labor force across the globe. In addition, women tend to have a predominant role at the household level related to food security. In this context, integrating measures to prevent and respond to Violence Against Women and Girls (VAWG) into food and agriculture projects is urgent. The aim of the brief is to provide an overview and avenues for actions that are to be identified and understood as effective entry points for practitioners and policy makers in addressing VAWG through agriculture operations. This brief provides guidance on ethics and safety, resources for carrying out a rapid situation analysis, specific and actionable ideas for the implementation of effective interventions at the institutional, sectoral, and community levels, detailed examples of promising practices, key areas for integrating VAWG prevention and response by key agriculture and food project sub-sector, a menu of indicators for use in monitoring and evaluation, and dozens of active links to more detailed resources and toolkits.
The arrival of a new government provides an opportunity to reinvigorate the reform agenda to deliver inclusive growth for the Somali people. Since the establishment of the Provisional Constitution in 2012, Somalia has made commendable progress on many fronts. Macroeconomic stability has been maintained, high levels of indebtedness are being addressed through the Heavily Indebted Poor Countries (HIPC) initiative, several sector laws and institutions have been established, and a poverty reduction strategy paper has been developed – the ninth National Development Plan (NDP9). However, much remains to be done and the time has come to mark the next milestone in Somalia’s development trajectory through advancing reforms anchored in the HIPC process. The objective of the collection of policy notes is to provide sector-specific policy advice for the leadership of the new government, drawing on the expertise of the World Bank Group. This overview chapter synthesizes the advice across the sector policy notes and is organized in four sections. The first section outlines the current context. The second section presents the framework for organizing the policy notes. The third section summarizes the advice, and the fourth section concludes.
Datasets accompanying the paper “Virtual Research Environments Ethnography: a Preliminary Study”, a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories. While for legal reasons we can not share the original datasets obtained by querying the databases, since they include copyrighted data, we can share the two datasets derived from the query results and the two topic modelling datasets. The dataset “main_dataset.csv” consists of the merged query results from ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink databases. It is structured into six columns: (i) doi; (ii) title; (iii) content_type; (iv) publication year; (v) keyword_search; (vi) DB. The ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘content_type’ label refers to the different and normalised typologies of resources: (a) Article; (b) Book, (c) Book Chapter; (d) Chapter; (e) Chapter ReferenceWorkEntry; (f) Conference Paper; (g) Conference Review; (h) Early Access Articles; (i) Editorial; (j) Erratum; (k) Letter; (l) Magazines; (m) Masters Thesis; (n) Note; (o) Ph.D. Thesis; (p) Retracted; (q) Review; (r) Short Survey; (s) Standards. (c) and (d) refer to the same type of entry (they are used in different databases), while in the case of (e) we observed that it is used in the Springer database to refer mainly to encyclopaedic entries. The ‘keyword_search’ label is used for identifying the keyword group used for formulating the query: (a) science gateway | scientific gateway; (b) virtual laboratory | Vlab; or (c) virtual research environment. The ‘DB’ label indicates the provenance of the entries from one of the five databases we selected for our study: (a) ACM; (b) IEEE; (c) ScienceDirect; (d) scopus; and (e) Springer, identifying the ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink respectively. The dataset “filtered_dataset.csv” consists of the deduplicated and filtered entries (journal articles and conference papers from 2010 onward, with a DOI assigned) from the “main_dataset.csv” we used as the final dataset for answering our research questions. It is structured into ten columns: (i) doi; (ii) title; (iii) venue; (iv) publication_year; (v) content_type; (vi) abstract; (vii) keywords; (viii) science gateway | scientific gateway; (ix) virtual laboratory | Vlab; and (x) virtual research environment. As for the previous dataset, the ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘venue’ label is used for indicating the conference or the journal the entries refer to. The values derive from the original query results. The ‘abstract’ and ‘keyword’ labels are used for the abstracts and the keywords associated with the entries. The values are mainly derived from the original query results, as we integrated the missing ones by querying OpenAIRE. The ‘science gateway | scientific gateway’, ‘virtual laboratory | Vlab’ and ‘virtual research environment’ labels indicate the connection between the entries and the keyword group used for denoting them. The values are binary (1 if the keywords belong to the group, 0 if they do not). The datasets “sg_vlab_vre_topics_datasets.csv” and “sgvlabvre_topics_dataset.csv” consist of the three datasets and of the unique dataset resulting from topic modelling, the first (corpus divided into three datasets) and the second analysis (corpus as a whole) respectively. They share the same structure: (i) Topic; (ii) #studies; (iii) Representative word; (iv) Representative word weight. The ‘Topic’ label is used for the topic denomination and the values consist of an alphanumeric string indicating the dataset and the progressive topic number: (a) SG, for the scientific gateway dataset; (b) VRE, for the virtual research environment dataset; (c) VLAB, for the virtual laboratory dataset; and (d) A, for the corpus as a whole. The ‘#studies’ label indicates the number of studies contributing to each topic. The ‘Representative word’ and ‘Representative word weight’ labels are used for denoting the keywords describing each topic and their weights respectively.
Datasets accompanying the study “A Taxonomy of Tools and Approaches for FAIRification” on the tools and approaches emerging from stakeholders’ experiences adopting the FAIR principles in practice. Datasets: queryResults.csv Description The dataset consists of the query results returned by OpenAIRE Explore and defines the corpus at the base of our study. Structure 11 columns: Query Type of query entered FAIR, FAIRification (all fields) OpenAIRE subjects (subject) Result Type [OpenAIRE label] Type of the research output (publication|data|software|other) Title [OpenAIRE label] Authors [OpenAIRE label] Publication Year [OpenAIRE label] DOI [OpenAIRE label] Download from [OpenAIRE label] Type [OpenAIRE label] Subtype of the research output Journal [OpenAIRE label] Funder|Project Name (GA Number) [OpenAIRE label] Access [OpenAIRE label] Access rights publicationsTools.csv Description The dataset pairs the tools/services extracted from the corpus to their respective source. Structure 2 columns: source reference to the publication or software citation name name of the tool/service/technology toolsAll.csv Description The dataset lists all the unique tool/service entries, distinguishing between those that were considered relevant for the study (further categorised into tools, technologies or services) and those that were excluded. Structure 3 columns: entryType entry categorisation (tool|service|technology|excluded) name name of the tool/service/technology URL URL of the tool/service web page or description toolsType.csv Description Classification of the tools/services/technologies into the study-defined classes. Structure 19 columns: name name of the tool/service/technology URL URL of the tool/service web page or description GUPRI helper - GUPRI creation and management service ‘class - subclass’ of the tool/service/technology GUPRI helper - GUPRI Indexing and discovery service ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata editor ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata extractor ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata tracker ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata validator ‘class - subclass’ of the tool/service/technology Metadata helper - Metadata assistant ‘class - subclass’ of the tool/service/technology Indexing and discovery service - registry ‘class - subclass’ of the tool/service/technology Indexing and discovery service - repository ‘class - subclass’ of the tool/service/technology Indexing and discovery service - Indexing and discovery service finder ‘class - subclass’ of the tool/service/technology Converter - metadata ‘class - subclass’ of the tool/service/technology Converter - data ‘class - subclass’ of the tool/service/technology Licence helper ‘class’ of the tool/service/technology Assessment tool - automated ‘class - subclass’ of the tool/service/technology Assessment tool - manual ‘class - subclass’ of the tool/service/technology Assessment tool - Assessment tool finder ‘class - subclass’ of the tool/service/technology DMP tool ‘class’ of the tool/service/technology toolsFAIR.csv Description The dataset relates the tool/service/technology to the FAIR principles it enables. Structure 12 columns: name name of the tool/service/technology URL URL of the tool/service web page or description F1 reference to the FAIR principle F2 reference to the FAIR principle F3 reference to the FAIR principle F4 reference to the FAIR principle A generic reference to the accessibility principles (see the paper) I1 reference to the FAIR principle I3 reference to the FAIR principle R1.1 reference to the FAIR principle R1.2 reference to the FAIR principle R1.3 reference to the FAIR principle toolsScope.csv Description Since the FAIR principles have been specified for different types of resources ((meta)data, semantic artefacts, software and workflows), the dataset correlates the tool/service/technology and the types of FAIR-specific resources it covers. Structure 6 columns: name name of the tool/service/technology URL URL of the tool/service web page or description (meta)data reference to the FAIR-specific resource semantic artefact reference to the FAIR-specific resource software reference to the FAIR-specific resource workflow reference to the FAIR-specific resource toolsDomain.csv Description Classification of the tools/services/technologies into the Frascati framework-defined domains. Structure 9 columns: name name of the tool/service/technology URL URL of the tool/service web page or description cross-domain domain Agricultural and veterinary sciences domain Engineering and technology domain Humanities and the arts domain Medical and health sciences domain Natural sciences domain Social sciences domain
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