Summary:

First release to publish to Zenodo DOI.

Summary:

This update to the COVID-19 Forecast Hub repository is a snapshot as of 4 December 2020 of the data hosted by and visualized at https://covid19forecasthub.org/.

Summary: Projections of COVID-19, in standardized format

Summary: Projections of COVID-19, in standardized format

Summary:

Data and code to accompany The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic

Summary:

This package presents the third chapter of my PhD thesis and contains supplemental figures and tables in two excel files and the Matlab code for all kinetic models presented.

Summary:

EEG Data for: "Composing only by thought: novel application of the P300 brain-computer interface". See the file "Information about the Dataset.pdf" for further information.

Summary: ZooSCAN images from BATS MOCNESS tows during R/V Atlantic Explorer cruises AE1614, AE1712, AE1830, and AE1819 in the vicinity of the Bermuda Atlantic Time-series Study (BATS) in July of 2016, 2017, and 2018 as well as October 2018 (eight casts in total, 63 discrete nets). These data were published in Maas et al. (2021). For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/853440

Summary: ZooSCAN images of zooplankton collected during OAPS MOCNESS tows during R/V Oceanus cruise OC473 in the Northwestern Atlantic in 2011 and R/V New Horizon cruise NH1208 in the Northeastern Pacific in 2012. Day and night stations were sampled between 0 to 1000m depths from 35 to 50 N in the northwest Atlantic in 2011, and from 35 and 50N along CLIVAR line P17N in 2012. Some chaetognaths and all pteropods were removed prior to imaging in association with the original OAPS and ancillary projects.

Summary:

This repository (in progress) includes the code and data to replicate the findings summarized in our paper.

Currently, it contains only the final data panels for the regression analysis and for creating the figures.

Code for scraping and processing the raw data and for building the panels will be added.

Summary:

Governments around the world are responding to the novel coronavirus (COVID-19) pandemic \cite{wu2020new} with unprecedented policies designed to slow the growth rate of infections. Many actions, such as closing schools and restricting populations to their homes, impose large and visible costs on society, but their benefits cannot be directly observed and are currently understood only through process-based simulations. Here, we compile new data on 1,717 local, regional, and national non-pharmaceutical interventions deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France, and the United States (US). We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of roughly 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different impacts on different populations, but we obtain consistent evidence that the policy packages now deployed are achieving large, beneficial, and measurable health outcomes. We estimate that across these six countries, interventions prevented or delayed on the order of 62 million confirmed cases, corresponding to averting roughly 530 million total infections. These findings may help inform whether or when these policies should be deployed, intensified, or lifted, and they can support decision-making in the other 180+ countries where COVID-19 has been reported.

Summary:

This repository (in progress) includes the code and data to replicate the findings summarized in our paper.

Currently, it contains only the final data panels for the regression analysis and for creating the figures.

Code for scraping and processing the raw data and for building the panels will be added.

Summary:

This repository (in progress) includes the code and data to replicate the findings summarized in our paper.

Currently, it contains only the final data panels for the regression analysis and for creating the figures.

Code for scraping and processing the raw data and for building the panels will be added.

Summary:

Data and code to accompany The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic

Summary:

Description:

This dataset consists of invertebrate abundance data and associated ecosystem measurements (Including leaf litter depth and mass, seedlings, soil moisture and nutrients, and rainfall) measured within an area of lowland, old growth dipterocarp rainforest in the Maliau Basin Conservation Area, Sabah, Malaysia between 2015 and 2016. Data were collected during a collaborative project which was included in the NERC Human-modified tropical forest (HMTF) programme.

Project: This dataset was collected as part of the following SAFE research project: Biodiversity and land-use impacts on tropical ecosystem function (BALI): Experimental manipulations of biodiversity at SAFE

Funding: These data were collected as part of research funded by:

• UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NERC grant NE/L000016/1)

This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

 

Permits: These data were collected under permit from the following authorities:

• Sabah Biodiversity Centre (Research licence na)

 

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This consists of 1 file: Termite_monitoring_maliau.xlsx

Termite_monitoring_maliau.xlsx

This file contains dataset metadata and 11 data tables:

1. Leaf_litter_depth (described in worksheet Leaf_litter_depth)

   Description: summary of leaf litter measurements collected on experimental plots. An in situ assay of ecosystem-level decomposition was carried out by measuring leaf litter depth during the drought (March 2016) and non-drought (October 2016) periods. Forty leaf litter depth measurements were taken in total per plot in March 2016, with 10 measurements spaced every 3 m across four 30 m transect lines, with each transect being separated by 10 m. In October 2016, a total of sixty measurements were taken per plot, similarly spaced out across a total of six 30 m transect lines

   Number of fields: 5

   Number of data rows: 800

   Fields:

   • Date: The month and year in which leaf litter depth was recorded (Field type: Date)
   • Plot: The experimental plot that the data were collected from (Field type: Location)
   • LINE: The sampling line within each plot a leaf litter measurement was taken (Field type: ID)
   • Depth_cm: The depth of leaf litter measured at each point (Field type: Numeric)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)

2. Leaf_litter_invertebrates (described in worksheet Leaf_litter_invertebrates)

   Description: In 2016 (two years after initial poisoning), fifteen 1 m2 leaf litter samples were collected from each plot. These were collected every 7m along a 100m transect. Sieved litter samples were suspended in Winkler bags for three days to extract invertebrates. All leaf litter invertebrates were identified to order and counted.

   Number of fields: 31

   Number of data rows: 120

   Fields:

   • Plot: The experimental plot that the data were collected from (Field type: Location)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)
   • Distance: Distance along the sampling transect in metres (Field type: ID)
   • Coleoptera_Adults: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Coleoptera_Larvae: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Diptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Hemiptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Araneae: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Opiliones: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Isopoda: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Oligochaeta: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Hymenoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Formicidae: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Mollusca: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Lepidoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Chilipoda: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Diplopoda: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Thysanoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Psocoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Dermaptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Orthoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Blattodea: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Leeches: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Plecoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Neuoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Trichoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Mecoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Odonata: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Siphonaptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Termites: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Pseudoscorpions: Frequency of inverts in leaf litter (Field type: Numeric trait)

3. Leaf_litter_mass (described in worksheet Leaf_litter_mass)

   Description: Decomposition rate was assessed using leaf litter decomposition bags. We collected freshly abscised Shorea johorensis leaf litter from trees close to our experimental plots for use in the leaf litter decomposition bags. The leaf litter was dried at 60 degrees Celsius until it reached a constant weight. We used 300-micron nylon mesh to produce macroinvertebrate exclusion bags, the closed-bag treatment, and created an open-bag treatment by cutting 10, 1 cm holes in each side of the 300-micron mesh bags to allow access to the material by termites and other macroinvertebrates. This approach avoided any unintentional bias due to the use of different mesh size. Each leaf litter bag contained on average 10.5 g ± 0.6 g of dried Shorea johorensis. We left litter bags on the forest floor for 112 days before collection. Bags were placed on plots at the beginning of the 2015 drought (August 2015) and again during the non-drought period (July 2016).

   Number of fields: 6

   Number of data rows: 87

   Fields:

   • Condition: The rainfall season in which leaf litter bags were deployed (Field type: Categorical)
   • Plot: The experimental plot that the data were collected from (Field type: Location)
   • Bag_treatment: The treatment applied to each leaf litter bag - open = accessible to invertebrates, closed = inaccessible to invertebrates (Field type: Categorical)
   • Plot_treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
   • Mass_loss: total leaf litter mass loss from each bag in grams (Field type: Numeric)
   • Proportion: the proportion of leaf litter mass loss from each bag (Field type: Numeric)

4. Non_target_inverts (described in worksheet Non_target_inverts)

   Description: non-termites were collected in 2014 (pre-drought and pre-suppression), 2015 (during the drought and the suppression) and 2016 (post-drought). We collected 1m2, leaf litter samples, sieved the leaf litter and extracted invertebrates with Winkler bags for three days.

   Number of fields: 5

   Number of data rows: 5040

   Fields:

   • Year: the year in which sampling occured (Field type: ID)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)
   • variable: the order of non-target invertebrates samples (Field type: Taxa)
   • value: the number of individuals belonging to each order (Field type: Numeric)
   • log: the log of the number of individuals belonging to each order (Field type: Numeric)

5. Seedling_survival_non_drought (described in worksheet Seedling_survival_non_drought)

   Description: Seedling mortality was assessed using a seedling transplant experiment. In July 2015, 200 individuals of a leguminous liana, Agelaea borneensis, were collected from the forest matrix surrounding our plots. Seedlings were selected from seedling mats resulting from a masting event in 2014. We selected individuals that had only their cotyledons and had not yet developed their first true leaves, and were roughly the same height. We are therefore confident that individuals were all of the same age and developmental stage and that we minimised confounding influences of genetic variability by using individuals from the same conspecific seedling mat. Seedlings were planted in the ground in July 2015 in the same grid of 25 used to assess soil moisture (n = 25 per plot), which was located within the central 50 m sampling area of experimental plots. Each seedling was separated by at least 5 m from the next closest seedling. To minimise the effect of stochastic disturbance-induced mortality as a result of transplantation shock, we used the number of individuals alive one month after the initial transplant as the baseline abundance. Survival of seedlings during the drought was assessed 11 months after transplantation, in June 2016. Following this assessment, the number of live individuals in June 2016 was used as a new baseline abundance. Survival during non-drought conditions was assessed 12 months later in June 2017

   Number of fields: 4

   Number of data rows: 64

   Fields:

   • plot: The experimental plot that the data were collected from (Field type: Location)
   • alive_2016: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)
   • alive.2017: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)

6. Seedling_survival_drought (described in worksheet Seedling_survival_drought)

   Description: Tree seedlings survival during the drought

   Number of fields: 3

   Number of data rows: 274

   Fields:

   • Plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • alive_2016: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)
   • Treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)

7. Soil_moisture (described in worksheet Soil_moisture)

   Description: Soil moisture was measured using a Delta-T Devices HH2 moisture metre in March and October 2016. Soil moisture was recorded at 25 points, spread evenly across each plot in a grid, with each sampling point separated by 5 m from the next point.

   Number of fields: 5

   Number of data rows: 400

   Fields:

   • plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
   • soil_moisture: the % soil moisture measured at each point (Field type: Numeric)
   • condition: The rainfall season in which soil moisture measurements were taken (Field type: Categorical)
   • date: the month and year in which the soil moisture recording was taken (Field type: Date)

8. Soil_nutrients (described in worksheet Soil_nutrients)

   Description: We used Plant Root Simulator (PRS®) resin probes to assess mineralization rates of plant available soil nutrients (NO3-, NH4+, P, K, Ca, Mg, Mn, Al, Fe, Zn) over a two-week period, during drought and non-drought conditions. In March 2016, we buried two anion and cation probe pairs at a random subsample of 12 points within the 25 sampling grid used to measure soil moisture. In October 2016, four probe pairs were placed at each point of the complete 25 sampling grid. We buried the probe membranes to a depth of 10 cm and left them in situ for two weeks, after which they were removed from the soil, cleaned with de-ionized water and subsequently analysed by Western Ag Innovations, Saskatoon, Canada.

   Number of fields: 15

   Number of data rows: 292

   Fields:

   • plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • Treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
   • Condition: the season in which the soil nutrient sampling occurred – drought (2015) or non-drought (2016) (Field type: Categorical)
   • NO3_N_micro_grams/10cm2/burial length: Soil NO3 at 10cm2 profile (Field type: Numeric)
   • NH4_N_micro_grams/10cm2/burial length: Soil NH4 at 10cm2 profile (Field type: Numeric)
   • Ca_micro_grams/10cm2/burial length: Soil Ca at 10cm2 profile (Field type: Numeric)
   • Mg_micro_grams/10cm2/burial length: Soil Mg at 10cm2 profile (Field type: Numeric)
   • K_micro_grams/10cm2/burial length: Soil K at 10cm2 profile (Field type: Numeric)
   • P_micro_grams/10cm2/burial length: Soil P at 10cm2 profile (Field type: Numeric)
   • Fe_micro_grams/10cm2/burial length: Soil Fe at 10cm2 profile (Field type: Numeric)
   • Mn_micro_grams/10cm2/burial length: Soil Mn at 10cm2 profile (Field type: Numeric)
   • Cu_micro_grams/10cm2/burial length: Soil Cu at 10cm2 profile (Field type: Numeric)
   • Zn_micro_grams/10cm2/burial length: Soil Zn at 10cm2 profile (Field type: Numeric)
   • B_micro_grams/10cm2/burial length: Soil B at 10cm2 profile (Field type: Numeric)
   • Al_micro_grams/10cm2/burial length: Soil Al at 10cm2 profile (Field type: Numeric)

9. Termite_cumulative_attacks (described in worksheet Termite_cummulative_attack)

   Description: We monitored termite feeding activity on the plots using untreated TPRs. Sixteen untreated TPRs were placed on each plot and were scored for termite attack on a 0 to 5 scale, where 0 is untouched and 5 is completely eaten. After one month, TPR were scored and replaced. Before they were replaced, we recorded the cumulative amount of TPR consumed on each plot and calculated the plot-level cumulative mean attack scores.

   Number of fields: 4

   Number of data rows: 120

   Fields:

   • month: The month in which termite attack scores were recorded (Field type: ID)
   • cumulative_consumption: The cumulative consumption rate for each plot (Field type: Numeric)
   • plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)

10. Termite_C_plot_SPI (described in worksheet Termite_C_plots_SPI)

    Description: To assess the relationship between rainfall and termite abundance, we carried out termite transects on control plots every 2 months from March 2016 to December 2016 and also at the beginning and the end of the experimental period in June 2015 and June 2017. Daily total rainfall was collected from Danum Valley forest reserve (4°57′53″ to 55″ N and 117°48′14″ to 30″E) from November 2010 to March 2017. Daily values were used to calculate total monthly rainfall in the region, and this was used to calculate 3-monthly Standardised Precipitation Index (SPI)[2] in the 'SPI' package in R. The SPI is a climatic proxy used to quantify and monitor drought; negative values indicate drier than average conditions, while positive values represent wetter than average conditions.

    Number of fields: 5

    Number of data rows: 32

    Fields:

    • Plot: the control plot on which samples were collected (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
    • total: total number of termite hits recorded (Field type: Numeric)
    • date: the month in which sampling occurred (Field type: Date)
    • SPI: the standardized precipitation index number calculated for each time period from rainfall data collected at Danum Valley Field Station (Field type: Numeric)
    • Wet.dry: the rainfall conditions at the time of sampling (wet = 2017, dry

Summary: ZooSCAN output from of imaged zooplankton collected during BATS MOCNESS tows during R/V Atlantic Explorer cruises AE1614, AE1712, AE1830, and AE1819 in the vicinity of the Bermuda Atlantic Time-series Study from 2016 to 2018. These data were published in Maas et al. (2021). For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/857891

Summary: ZooSCAN biovolume to biomass from the Sargasso Sea including locations in the vicinity of the Bermuda Atlantic Time-series Study (BATS). Samples were collected during MOCNESS tows during R/V Atlantic Explorer cruises between 2016 to 2019 (AE1614, AE1712, AE1830, AE1917, AE1918, AE1931) and a few small boat deployments. These data were published in Maas et al. (2021) as Supplementary Table 1. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/854077

Summary: Supplemental data for Reducing effort in the U.S. American lobster (Homarus americanus) fishery to prevent North Atlantic right whale (Eubalaena glacialis) entanglements may support higher profits and long-term sustainability. Figure 5: Estimated North Atlantic right whale population, number of calves, observed mortalities and serious injuries, and diagnosed cause of death or serious injury. Diagnosed entanglements have increased significantly since the population has been in decline. Data from Waring et al. 1997, Kraus et al. 2001, Waring et al. 2002, Moore et al. 2004, Waring et al. 2015, Pace et al. 2017, Pettis et al. 2018, Hayes et al. 2018b, and NOAA Northeast Fisheries Science Center (unpublished). Figure 15: Maine and Nova Scotia (NS) Maritimes lobster landings and landings per trap from 1990 to 2017. While NS Maritimes landings per trap mirrored landings growth, Maine landings per trap remained relatively stagnant for the majority of this period. Data from DFO Seafisheries Landings, DFO Atlantic Region Licences, DFO Integrated Fisheries Management Plan for LFAs 27-38 (2011), NMFS Annual Commercial Landings Statistics, and Maine Department of Marine Resources Historical Maine Lobster Landings. Figure 16: Maine lobster landings per trap, number of traps (an upper bound indicated by the number of trap tags sold), and total landings weight from 1986 to 2017. Landings per trap were relatively stagnant except from 2007 to 2013, when landings per trap increased substantially year on year, correlating with a decrease in the number of traps and faster rate of growth in total landings. Data from National Marine Fisheries Service Annual Commercial Landings and Maine Department of Marine Resources Historical Maine Lobster Landings. Figure 18: Annual lobster landings weight, in millions of pounds, for Massachusetts Statistical Reporting Areas (SRAs) 5, 6, 7, 8, and 9 from 1990 to 2017. Vertical line indicates the start of the Massachusetts Restricted Area Trap/Pot Closure in 2015. The Massachusetts Restricted Area Trap/Pot Closure includes all of SRAs 6, 7, 8, and 9, as well as most of SRA 5 and small portions of SRAs 18 and 19. Data from the Massachusetts Division of Marine Fisheries (unpublished). Figure 20: American lobster commercial landings weight standardized to 1990 in the primary Statistical Reporting Areas (SRAs) covered by the Massachusetts Restricted Area (SRAs 5, 6, 7, 8, and 9) and the rest of the state of Massachusetts. The Massachusetts Restricted Area seasonal trap/pot fishery closure took place on February 1st, 2015. Data from Massachusetts Division of Marine Fisheries (unpublished) and National Marine Fisheries Service Annual Commercial Landings Statistics. Figure 21: Lobster landings weight in the Statistical Reporting Areas (SRAs) covered by the Massachusetts Restricted Area (5-9) and to the north (1-4) and south (10-14) from 1990 to 2017. Relative growth in landings in SRAs 5 to 9 was stronger than in neighboring areas since the closure was implemented. Vertical line indicates the start of the three-month closure in 2015. Data from Massachusetts Division of Marine Fisheries (unpublished). Figure 23: Lobster landings value in February, March, and April from Massachusetts Statistical Reporting Areas 5 to 9, 2005 to 2018. Landings value from these areas dropped approximately $94,000 from the period immediately before to the period immediately after the closure was implemented. Vertical line indicates the start of the Massachusetts Restricted Area trap/pot closure in 2015. Landings value calculated by multiplying landings weight for each area by average price for Massachusetts for that month and year. Value is nominal and not adjusted for inflation. Data from Massachusetts Division of Marine Fisheries (unpublished).

Summary: Gravity, magnetic, and bathymetry data collected along a continuous 1400-km-long spreading-parallel flow line across the Mid-Atlantic Ridge indicate significant tectonic and magmatic fluctuations in the formation of oceanic crust over a range of timescales. The transect spans from 28 Ma on the African Plate to 74 Ma on the North American plate, crossing the Mid-Atlantic Ridge at 35.8 ºN. Gravity-derived crustal thicknesses vary from 3–9 km with a standard deviation of 1 km. Spectral analysis of bathymetry and residual mantle Bouguer anomaly (RMBA) show diffuse power at >1 Myr and concurrent peaks at 390, 550, and 950 kyr. Large-scale (>10-km) mantle thermal and compositional heterogeneities, variations in upper mantle flow, and detachment faulting likely generate the >1 Myr diffuse power. The 550- and 950-kyr peaks may reflect the presence of magma solitons and/or regularly spaced ~7.7 and 13.3 km short-wavelength mantle compositional heterogeneities. The 390-kyr spectral peak corresponds to the characteristic spacing of faults along the flow line. Fault spacing also varies over longer periods (>10 Myr), which we interpret as reflecting long-lived changes in the fraction of tectonically- vs. magmatically- accommodated extensional strain. A newly discovered off-axis oceanic core complex (Kafka Dome) found at 8 Ma on the African plate further suggests extended time periods of tectonically dominated plate separation. Fault spacing negatively correlates with gravity-derived crustal thickness, supporting a strong link between magma input and fault style at mid-ocean ridges.