The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

The sentencing data presented in this report reflects the judgement imposed by the court on people that have been found guilty. The data is recorded by count, meaning by each individual cause of action, and each count receives a sentence. Included in this data set are the defendant counts by city/suburb and sentence, their associated offense type, and year.

Abstract

The PFR activities to be evaluated at end-line consists mainly of demarcation and registration of land parcels (under customary tenure) as Titre Foncier or an Attestation de Droit Coutumière. The impact evaluation aims to quantify and analyse impact of these interventions on productivity and food security disaggregated by target groups and gender.

The research questions to be answered after the endline data collection are:

1) Do PFRs (or ADCs) contribute to a perception of greater land tenure security? 2) Does improved tenure security lean to a growth in agricultural investment and/or changes to management of land? 3) Do PFRs improve access to land and rights over land among marginalised groups (women, youth and migrants)? 4) Do PFRs lead to an increased number of land transactions? 5) Does increased land security address existing constraints on land markets and lead to more efficient allocation of land resources and thereby an increase in productivity? 6) Do property rights and improved user rights result in better access to credit, possibly allowing for income diversification and thus increasing household welfare? 7) Do the new arrangements put in place during the implementation of the PFRs facilitate the resolution of land conflicts, or even prevent the emergence of these land conflicts?

Geographic coverage

The clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Analysis unit

• Villages
• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation consists of gender and youth disaggregated data collection at base line, before the start of the intervention, in both the treatment and control villages. End line data will be collected at least 2 growing seasons after issuing of documentation to farmers.

The sample consisted of 2968 households, which were taken from 26 villages selected for the implementation of a Plan Foncier Rural (PFR), or rural landholding plans, these were the treatment villages and 27 control villages that did not benefit from a PFR.

The treatment villages were assigned by the ProPFR team in geographic clusters. The assignment of control villages followed this geographic clustering, also using further village level data with the aim of finding similar villages to maximize comparability. These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Villages were selected from 11 geographical clusters of villages facing similar issues, allowing easier logistical planning for the rollout of the PFRs.

Villages selected to be part of the programme had the following characteristics: • Bordering/near to a classified national forest • At high risk of land grabbing, • The presence of another GIZ supported SEWOH project1 • Agropastoral areas (in particular the presence of transhumance –cattle driving - corridors)

But should not have the following: • Villages bordering Nigeria, within the band of increased security • MCA intervention with a PFR • Suffered serious conflict which could block the realisation of a PFR, or where a PFR may reignite past conflicts.

These characteristics alongside the desire of the implementing team to select villages in clusters, for practical reasons presented the first challenge in selecting suitable comparison villages to measure the impact of the ProPFR programme. Clustering meant that villages selected for comparison should be near the clusters to be comparable, but given the typical geography of villages in northern Benin, in that most people live in the village centre rather than spread evenly with sufficient density at the village boundary, and the lack of clearly defined village boundaries, a geographic discontinuity could not be exploited.

The second challenge in selecting comparison villages arose due to a change in the village definitions in 2013, when Benin changed from 3758 to 5290 villages which is often referred to as the “nouveau découpage”. Some old villages were split but there are no clearly defined village boundaries for the new set of villages. ProPFR selected from among the new villages, so the control villages also needed to be selected from this list. Given that the last census was collected prior to this new definition of villages, no data about the villages existed that could easily be used in matching villages to those selected for the ProPFR.

Due to this lack of data on the characteristics of the people residing in the villages, Geographical Information Systems (GIS) data were used to match each of the treatment PFR villages to a control village. Villages which were previously included in the MCA’s wave of PFRs were excluded from our study due to the difficulty in separating the effects of the two programs (MCA vs ProPFR). For each PFR village, a buffer of 20km was drawn and the union constructed for each cluster. Within this area, other villages were considered as a potential control village. Of the selection criteria, the only one applicable from GIS data is the proximity to a national forest. Where villages were close to a national forest, we attempted to match it with a control village also close to a national forest. The additional criteria on which villages were matched were the proximity to a main road (as classified by the Open Street Map shapefiles for roads) and the number of buildings in the central agglomeration of a village. Main roads are used as a proxy for access to markets and thereby potentially income levels.

The size of a village and the amount of land which can be used around it will be influenced by the size of the population as well as the presence of national forests. This strategy is similar to a Coarsened Exact Matching (CEM) strategy (see Blackwell et al, 2009), in which key characteristics are reduced (perhaps from continuous variables) to a small number of categories and matched with one another exactly. In our selection of villages, one control village was selected for each treatment village based on the key characteristics, defined as proximity to national forests (5km) and main roads (1km), and having a similar number of buildings (within 1km of the central point).

For a small number of villages, we faced an issue of common support, meaning there were no exact matches on the key characteristics. In this case other nearby villages were selected which fulfilled as many of these characteristics as possible. Data were collected on a wide range of variables following the theory of change, which states that the improvements in institutions and the PFRs may lead to improved perceived land tenure security and improved access to land for women and young men through the activities carried out by the ProPFR team. This perceived land tenure security is often seen as key to agricultural investments and thereby food security in the long term, as it allows long-term planning. The issuing of official documentation provides collateral for a loan should households wish to borrow and invest in productive activities or smooth consumption.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Survey comprised two questionnaires namely:

1. Household Questionnaire: Which comprised 14 modules with 7 rosters. Modules include household members, employment and enterprises, durable goods, housing, census of non-agricultural plots, agricultural plots, land donations, land sales, land losses, perceptions on land tenure, participation in PFR, loans, food security, young men and women.

2. Community (village) questionnaire: The community survey was administrated to each village in the form of small group interviews to collect information on the socio-economic characteristics of these villages, local land tenure structures and practices, and local prices on agricultural inputs and production. The questionnaire was organized in 9 modules: characteristics of the survey participants, land tenure, land use, land market, land conflicts, other village structures and interventions, agriculture, PFR, and village chief. The characteristics of the participants were recorded in a separate roster.

The extensive household survey was first asked to the household head with additional modules to be answered by the wife of the household head (or the female household head) as well as a young male (defined as an unmarried man, aged 18-35).

Cleaning operations

Various consistency checks were performed to ensure data quality, including systematic reports of contradictory answers and of extreme values. Throughout the data collection process, two main issues were reported. The first pertains to the sampling methodology of buildings, that led to the necessary replacement of pre-selected non-housing buildings. However, just short of 500 households required replacement. The majority of the buildings replaced were not residential buildings and were therefore not eligible for inclusion in the survey. These were replaced by the next building in the random order of buildings. The number of buildings for which nobody could be found for surveying was very low (23), thanks to the robust replacement protocol.

The second issue concerns the refusal of the village Sombouan 2 to participate in the survey. Despite several attempts, this village had to be excluded from the survey. The data were also examined for missing information for required variables, and sections. Any problems found were then reported back to the supervisors where the correction was then made.

Response rate

The response rate for

The sentencing data presented in this report reflects the judgement imposed by the court on people that have been found guilty. The data is recorded by count, meaning by each individual cause of action, and each count receives a sentence. Included in this data set are the defendant counts by race and sentence, their associated offense type, and year.

Overview of the included physical environmental variables, their mean values at baseline and follow-up and the mean change scores.

Analysis of ‘Statewide Commercial Baseline Study of New York Means of Energy Using Equipment: 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2333ea0d-9c8d-4da6-98d7-1109197f552d on 12 February 2022.

--- Dataset description provided by original source is as follows ---

The overall objective of the Statewide Commercial Baseline research was to understand the existing commercial building stock in New York State and associated energy use, including the means of energy using equipment. This dataset provides all characteristics that are presented as averages, such as the average square footage of businesses or the average cooling capacity of split systems. All supporting summary statistics are also provided. For more information, see the Final Report at https://www.nyserda.ny.gov/About/Publications/Building-Stock-and-Potential-Studies/Commercial-Statewide-Baseline-Study

NYSERDA offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

--- Original source retains full ownership of the source dataset ---

Metal, hydrocarbon, or nutrient data have not been recorded for the Arctic coastal plain 1002 area of the Arctic National Wildlife Refuge (Arctic Refuge) in areas of prospective oil and corridor development. Pre-development baseline data for contaminants are necessary to enable general characterization of water quality and contaminant residues, as well as to provide site-specific pre-development information in the event of a Congressional decision to open the Arctic coastal plain to oil and gas exploration and development. This study examines 1988-1989 samples of sediments, water, sedge, birds, invertebrates, and fishes from the 1002 area. Volume 1 of the three volumes in this report describes the study area, study sites, methods, and objectives, and provides summary statistics (geometric mean, arithmetic mean, arithmetic standard deviation, maximum, minimum, and median) for those analytes with more than 2/3 of the concentrations greater than the limit of detection. Volume 2contains the raw metal and hydrocarbon contaminant data, and the raw water quality data. Volume 3summarizes quality assurance/quality control (QA/QC) results which include mean relative percent differences (RPD's) from duplicate analyses, mean percent recoveries from spiked analyses, mean recoveries and Z scores from standard reference material analyses, and maximum concentrations from blank analyses. For a comprehensive description of all quality assurance/quality control methods, also see Volume 1. These reports provide a database on a sufficient number of aquatic, terrestrial, and lagoon samples to enable general characterization of water quality and contaminant residues, as well as to provide site specific pre-development information. The reader is strongly encouraged to use the QA/QC data in Volume 3 to assess data quality on an analyte-by-analyte basis for each sample matrix. This information will be used by Refuge management and State and Federal regulators to assess any post development changes that result from any oil and gas exploratory or production activities. The data will also be useful in evaluating special use permits, Clean Water Act Sections 402 and 404 permits, and State wastewater permits, and in recommending appropriate mitigation measures if development occurs on the 1002 area.

The sentencing data presented in this report reflects the judgement imposed by the court on people that have been found guilty. The data is recorded by count, meaning by each individual cause of action, and each count receives a sentence. Included in this data set are the defendant counts by gender and sentence, their associated offense type, and year.

We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

This data release contains reference baselines for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each transect were associated with each transect. Next, it was determined which points were one the foreshore, and then a linear regression was fit through the foreshore points. Beach slope was defined as the slope of the regression. Finally, the regression was evaluated at the elevation of Mean High Water (MHW) to yield the location of the shoreline. In some areas there was more than one lidar survey available; in these areas the slopes from each survey are provided. While most of t ...

Overview of the included health-related variables, their mean values at baseline and follow-up and the mean change scores.

Mean discrepancy and accuracy of the predictions obtained by the Markov original (OM) and simplified models (SM1 and SM2).

This is the data for the ICPR 2020 paper ICPR 2020 Competition on Text Block Segmentation on a NewsEye Dataset. The data is taken from the NewsEye project and consists of historical newspaper pages (partially binarized) ranging from the 19th to 20th century provided by the Austrian National Library, i.e., especially newspapers in German language. The newspapers made available for this competition comprises the titles "Arbeiter Zeitung", "Illustrierte Kronen Zeitung", "Innsbrucker Nachrichten" and "Neue Freie Presse". The data is split into two tracks. A simple track with newspaper pages only with continuous text (40 pages training data, 10 pages test data) and a complex track with pages including additional tables, images or advertisements (40 pages training data, 10 pages test data). The training data (simple_pages_train.zip, complex_pages_train.zip) contains a set of scanned pages. Furthermore, for every image we provide the coordinates of the baselines, the corresponding text of the lines and the text regions marking the text blocks in the well-established PAGE XML format. Additionally, baselines lying within the same block have a unique ID in the so-called "custom tag". Please note that a text block caputers a whole paragraph and the block outlines enclose the text very closely. Headlines are separately marked and blocks are not across columns. Furthermore, images can be ignored since they (usually) do not contain baselines and occurring tables and framed advertisements are handled as single text blocks. The following represents a snippet of a PAGE XML file where the baseline with ID "tl_223" forms a block together with all other lines with the block ID "a7"

Data in this collection include annual and monthly summaries of oceanographic and climatological variables, as well as ecological monitoring variables, for 155 California MPAs, 4 California bioregions, and MPAs aggregated by bioregion. Oceanographic and climatological variables (sea surface temperature, turbidity, net primary productivity, wave height, wave power, wind speed, surface and bottom aragonite concentrations, upwelling indices) were extracted from publicly available gridded spatial datasets by spatially masking and aggregating data using polygon shapefiles of each area of interest. Ecological variables were obtained from long-term monitoring datasets collected by multiple research groups. This submission includes 2 data tables: (1) dataone_annual_mean_table.csv which contains annual mean values of oceanographic and climatological values, as well as ecological monitoring variables, for 155 California MPAs (2) dataone_monthly_mean_table.csv which contains monthly mean values of oceanographic and climatological values for 155 California MPAs The boundaries for the MPAs referenced in this dataset and information on related long-term California MPA monitoring efforts can be found here: https://opc.dataone.org/view/doi:10.25494/P6V884

Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we show steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for regional climate change impact studies. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the differential strengths of global downscaling datasets. We also explored the patterns and magnitude of change for these regional projected climate shifts to determine their plausibility as future climate scenarios using Hawaiʻi as an example region. While our ensemble projec ...

Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.

This dataset describes the baseline cropping systems of Ecuador and the associated pedoclimatic conditions. A high spatial resolution approach was used to quantify the spatially-explicit theoretical and technical potential yield of ten key crop residues, as well as the inputs to soil and potential supply to the bioeconomy in terms of carbon. Besides, it provides future meteorological data (mean temperature and evapotranspiration) under the representative pathway concentration RCP4.5, in a monthly timestep, spatially explicitly assigned to the cropping systems defined within this baseline. The original data was extracted from the Agricultural and Livestock Public Information System (ALPIS; MAG, 2023) of Ecuador. The potential yields were calculated as the average data reported by the National Continuous Agricultural Production and Surface Survey (NCAPSS) for the period 2002-2019. Residue to product ratios (RPR), root to shoot (R:S), root distribution factors in soil profile, crop residues composition are parameters influencing the final C calculation and were determined based on literature review and accompany this database in order to keep the transparency of the data. This database provides all the baseline data required to perform long-term simulations of soil organic carbon for Ecuador.

Social isolation/loneliness groups by covariates and outcomes measures: Weighted percentages (within each group) or weighted means.

Grey rows contain data for surface waters and white rows the data for bottom waters.

Available onlineCall Number: [EL]Physical Description: 38 p.