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.

Data are means (SD) or numbers (%).Baseline data of participants who completed the study.

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 The New York State Energy Research and Development Authority (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 reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Baseline data on the study population (n = 45). Data are mean ± SD, or adjusted means from multivariate models with respective 95% confidence intervals.

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.

Data are mean (± SD), number (%), or median. Parameters at baseline were compared by using t-test. Respective p values are indicated. £Data available for 10 patients in the IFN-β group and 8 patients in the group not having received DMT. RNFL, retinal nerve fibre layer; IFN-β, interferon-β; DMT, disease modifying treatment; dB, decibel.

Baseline data on tree height and diameter at breast height (DBH) of T. distichum and T. ascendens saplings prior to planting in 2012 (means ± S.E., n = 9).

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.

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). Aside from these typical bioclimatic variables, we also calculated annual and mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate. 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.

*n (%).#mean (standard deviation).†Some missing data.‡Late referral defined as referral to nephrologist >3 months before dialysis start.Baseline patient characteristics.

The raw baseline and learning data of the TD age-groups. The table shows mean baseline data (threshold on Day1) and the average learning data (improvement from Day1 to Day5 expressed in degree) of the TD participants.

N is the number of participants; values with parentheses are mean (standard deviations). CDR-SB: Clinical Dementia Rating, sum of boxes score; ADAS-Cog: cognitive subscale of the Alzheimer’s Disease Assessment Scale; MMSE: Mini Mental State Exam. Aβ and ptau: cerebrospinal fluid (CSF) densities of these proteins (see also Table 3); HC: cognitively healthy; Aβ+ means ≤192 pg/ml; ptau+ means ≥23 pg/ml.*Excluding two who had converted to MCI at 24-months.

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.

Previous analyses of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) identified four main dietary patterns (DP). The aim of this study was to explore the association between the previously defined DP and renal function (RF). A cross-sectional study using the ELSA-Brasil baseline data was carried out. DP (“traditional”, “fruits and vegetables”, “bakery”, and “low sugar/low fat), metabolic syndrome (MS) using the Joint Interim Statement criteria, microalbuminuria (MA), and glomerular filtration rate (eGFR) through the CKD-EPI equation were evaluated. Abnormal RF was defined as eGFR<60 mL·min-1·(1.73 m2)-1 and MA≥3.0 mg/dL. Factors associated with RF were determined and mediation analysis was performed to investigate the association between DP, MS, and RF. A total of 15,105 participants were recruited, with a mean age of 52±9 years; 8,134 participants (54%) were females. The mediation analysis identified indirect associations between “bakery” and “fruits and vegetables”, and both were associated with decreased eGFR and albuminuria in both genders, compared with “traditional” and “low sugar/low fat” patterns in the general population. There was a direct association of the “bakery” pattern with MA in men (OR: 1.17, 95%CI: 1.92-1.48). The “fruits and vegetables” pattern also showed a direct association with reduced eGFR in women (OR: 1.65, 95%CI: 1.28-2.12), although there was no significance after adjustment. The “fruits and vegetables” and “bakery” DPs were associated with renal dysfunction. The only independent, direct association was between “bakery” DP and MA in men, raising concerns about DP and renal damage in men.

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 avail ...

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

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

Abstract

As part of the Adaptive Safety Net Project, the Government of Niger (with support from the World Bank and the Sahel Adaptive Social Protection Program) launched the implementation of productive inclusion measures to foster more productive livelihoods and improve resilience of cash transfer beneficiary households. This dataset covers three rounds of household surveys from the impact evaluation of these productive inclusion measures among cash transfer beneficiary households. It is published along with the related paper: Bossuroy, Thomas; Goldstein, Markus; Karimou, Bassirou; Karlan, Dean; Kazianga, Harounan; Pariente, William; Premand, Patrick; Thomas, Catherine; Udry, Christopher; Vaillant, Julia; Wright, Kelsey. 2022. "Tackling Psychosocial and Capital Constraints Opens Pathways out of Poverty".

Geographic coverage

The study focuses on a sub-sample of communes in all five regions chosen for the second phase of the Niger Adaptive Safety Net project (Dosso, Maradi, Tahoua, Tillabery, and Zinder). 17 communes were selected for the study, covering 322 villages across the 5 regions where cash transfer beneficiaries were eligible to receive complementary productive inclusion measures. In each sample village, approximately 14 households (maximum 15) were interviewed at baseline.

Analysis unit

Households as well as individuals within households.

Universe

Only households that are beneficiaries of the national cash transfer, located in communes and villages mentioned above

Kind of data

Sample survey data [ssd]

Sampling procedure

Cash transfer beneficiary households were chosen by either proxy means testing, community-based targeting, and a formula to proxy temporary food insecurity (as described in Premand and Schnitzer, 2021). 22,507 cash transfer beneficiary households were later assigned to either a control group or 3 productive inclusion treatment arms (Bossuroy et al., 2022). All three treatment arms include a core package of group savings promotion, coaching, and entrepreneurship training, in addition to the regular cash transfers from the national program. The first variant also includes a lump-sum cash grant (“capital” package). The second variant substitutes the cash grant with psychosocial interventions (“psychosocial” package). The third variant includes the cash grant and the psychosocial interventions (“full” package). The control group only receives the regular cash transfers from the national program. 4,712 households were drawn into a sample for data collection (1206 households in control, 1191 households in capital, 1112 households in psychosocial and 1203 households in full). Before the study, we conducted power calculations assuming an ICC of 0.10 (based on data from Ghana and a Niger national household survey) and equal sized arms. To maximize power, we sampled all villages in this phase. Sampling 15 households per village allowed for minimum detectable sizes of 0.057 SD between arms, before adjusting for baseline outcomes or strata.

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

Household surveys were collected in 3 survey rounds as described above.

The questionnaires included the following sections:

I. Beneficiary section Roster Health Beneficiary activity Household business Time use Finance Housing Food security Cash transfers Relationships Mental health Treatment measures II. Household head section Food consumption Head of household activities Relationships Agriculture Livestock and Fish Assets Education and Health spending Non food consumption Other programs Household transfers Shocks

Questions are generally consistent across rounds.

The data includes process variables, see attachment for variable definitions and Bossuroy et al. (2022) for details.

Cleaning operations

Survey data are labelled, deduplicated and cleaned. It includes constructed variables. The data is documented in three files. A household panel dataset shows data from the baseline, midline, and end-line surveys where observations missing at in the baseline survey are replaced with strata means. Households are observed in two periods. A household-level file shows select variables from the baseline survey. Finally, a food-level file shows median food prices per food unit.

Variables were constructed according to a pre-analysis plan, registered at https://www.socialscienceregistry.org/versions/52534/docs/version/document, and are further described in Bossuroy, et al (2022).

Response rate

The original sample included 4712 households. The baseline, endline, and end-line samples include 4608, 4476, and 4303 households, respectively, and thus completion rates of 97.8%, 95.0%, and 91.3%.

UKCP09 Regional values Monthly Averages - Mean air temperature (°C) Long-term averages for the 1961-1990 climate baseline are also available for 14 administrative regions and 23 river basins. They have been produced for all the monthly and annual variables, apart from mean wind speed, days of sleet/snow falling, and days of snow lying, for which data start after 1961. Each regional value is an average of the 5 x 5 km grid cell values that fall within it. The datasets are provided as space-delimited text files.

The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/.

The data files are obtained by clicking on the links in the table below. Each text file contains values of the 1961-1990 baseline average for each administrative region and for each river basin. Monthly variables have 12 values for each region (one for each month) whereas annual variables have just one value (the annual average).

To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.

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 the slopes are for sandy beaches, there is some slope data from rocky headlands and other steeper beaches. These data files (referenceLine_WestCoast.csv and referenceLine_WestCoast.shp) contain information about the reference baseline, the cross-shore transects, and the Mean High Water values used to estimate the shoreline. The accompanying data files (slopeData_WestCoast.csv and slopeData_WestCoast.shp) contain the slope data. The csv and shapefiles contain the same information, both file types are provided as a convenience to the user.