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Sharing the Evernote account with everyone in the company who wants access has encouraged ambient learning scenarios, in which people in all departments are browsing through our research and stumbling upon insights they never knew existed. As he was working on a new iOS interface, Aquazzura Woman Pandora Embellished Suede Ballet Flats Black Size 40 Aquazzura ok19P
, a designer in our MobileLab team, was curious which stats are most important to customers looking at campaign reports. He did a quick search in Evernote, and stumbled upon a chart from our 2013 survey sent to thousands of customers. He could see a clear ranking of the stats that customers use the most. He used this data to create a poster showing the hierarchy, which helped him make smarter design decisions driven by user research.

Now that everyone has access to the data, everyone is a researcher.

Even though the power of Connected UX is amazing, it’s easy for insights to languish in obscurity unless you’re regularly pulling them out and sharing them with your team.

Rather than wait for a project to provide this motivation, every other week we compose an email that goes to the entire company sharing interesting stats and broad trends. We rotate authorship to make sure many perspectives are represented. Each email concludes with an invitation to start contributing data—or simply browse out of curiosity.

We tried a lot of different solutions before landing on Evernote as our data hub. Wikis and custom databases always seemed too technical, and were bound to alienate some people who would love to contribute or just lurk. Your organization might find a simple Drupal install or a custom database works best. The storage tool really doesn’t matter, so long as it helps you adhere to these basic principles:

Any hindrance, no matter how small, preventing anyone from contributing or browsing data will kill the process. People shouldn’t have to learn new systems to be involved. Contributing data via email is perfect, because it requires no additional learning. Using a consumer software solution is also advantageous because many people will have experience with it. Eliminate all barriers to participation to get lots of people involved.

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and extending the desktop experience into every part of our lives. By making your data accessible across multiple devices, you’ll find insights will happen more routinely—in the line at the grocery store, in meetings, or on the couch in the evening. Ubiquity of access makes ambient learning easier.

Give everyone in your company access to the data and diligently invite contributions. It’s important that the data is open and shared so teams are encouraged to collaborate. From this collaboration you’ll find the most mind-boggling insights you would’ve otherwise never discovered.

By connecting disparate data, you’ll discover trends in seemingly disconnected things. That’s exactly what has us so excited. We’re finding patterns between departments, and among customers. We’re breaking down the silos that separate data streams and the teams that manage them.

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Impact factor: 4.18 Online ISSN: 1942-2466

is a Gold Open Access journal that publishes original research articles advancing the development and application of models at all scales in understanding the physical Earth system and its coupling to biological, geological and chemical systems.

Volume 10, Issue 5
May 2018
Cross‐lead error covariance matrix C(i, j) given by equation (3) for the real‐time CFSv2 forecasts of the Niño 3.4 index for monthly forecasts during the period 2011–2015. The axes give the lead time in units of days. Plot (a) shows the cross‐lead error covariance matrix for Niño 3.4 estimated directly from CFSv2 real‐time forecasts initialized at 0Z. Plot (b) shows the corresponding parametric model for cross‐lead error covariance matrix shown in Figure 4a. Similarly, plot (c) shows the cross‐lead error covariance matrix as in Figure 4a, but for real‐time forecasts initialized 4 times per day (0, 6, 12, 18 Z). Plot (d) shows the cross‐lead error covariance matrix for four initializations per day inferred from the parametric model fit at 1 day intervals.
Schematic diagrams of initiation processes of the MJO‐like disturbances, showing (top) moistening in the midtroposphere from day −9 to day −5 and (bottom) triggering and organization of convection associated with the MJO‐like disturbances from day −4 to day 0.
Parameter sensitivities. Global distributions of vertically averaged ensemble spreads of 6 h (a–e) atmospheric temperature (unit: K) and (f–j) specific humidity (unit: g/kg), and the vertical variations of the globally averaged ensemble spreads ((g–o) for the temperature; (p–t) for the specific humidity). The ensemble spread of model state based on a perturbed parameter ensemble serves as a measure of the model's sensitivity to examined parameter. Each row represents the results for a single parameter.
CRTM‐simulated (17 September 0100 UTC) and (a) F16 SSMIS observed brightness temperatures (K) at SSMIS channel 9 (183 ± 7 GHz). (b) Uses cloud scattering properties with microphysics‐consistent spheres for all ice species (CRTM‐DS). (c, d) Results when spheres are substituted with sector snowflakes for all ice precipitation species (snow and graupel) and for only the snow species, respectively. (e, f) Use the same cloud scattering properties as Figure 3d (only the snow species using sector snowflake scattering properties) but with half of the snow and graupel water content, respectively.
Figure 1. Schematic illustration of (left) soil heat transport and (right) soil water transport. F is the soil heat flux, including heat conduction, convection of sensible heat with flowing liquid water, transfer of sensible heat and latent heat by diffusion of water vapor, these components are represented by subscript T, q(liq),SH, q(vap),SH and q(vap),LH, respectively. q is the water flux, including liquid water and vapor flux due to hydraulic potential (Ψ) gradient (which are represented by subscript Ψ,liq and Ψ,vap, respectively) and liquid water and vapor flux due to soil temperature (T) gradient (which are represented by subscript T,liq and T,vap, respectively). S is the sink term including transpiration. The red color in subscript means the impacts of heat transport on water flux, blue color in subscript means the impacts of water transport on heat flux.
Change in MSE (colors) and divergence of MSE flux (contours) owing to convective gustiness for JJA. Both h and ∇⋅(vh) are scaled by (1/Cp), where Cp is the specific heat of dry air at constant pressure, such that Δh is given in units of K and Δ(∇⋅(vh)) is given in units of K/d. All values are zonally averaged over 110–150E.
Present‐day annual precipitation amount median for CAM at (a, b) 110 km and (c, d) 28 km horizontal resolutions of (a, c) large‐scale and (b, d) convective contributions.
Cross‐lead error covariance matrix C(i, j) given by equation (3) for the real‐time CFSv2 forecasts of the Niño 3.4 index for monthly forecasts during the period 2011–2015. The axes give the lead time in units of days. Plot (a) shows the cross‐lead error covariance matrix for Niño 3.4 estimated directly from CFSv2 real‐time forecasts initialized at 0Z. Plot (b) shows the corresponding parametric model for cross‐lead error covariance matrix shown in Figure 4a. Similarly, plot (c) shows the cross‐lead error covariance matrix as in Figure 4a, but for real‐time forecasts initialized 4 times per day (0, 6, 12, 18 Z). Plot (d) shows the cross‐lead error covariance matrix for four initializations per day inferred from the parametric model fit at 1 day intervals.
Schematic diagrams of initiation processes of the MJO‐like disturbances, showing (top) moistening in the midtroposphere from day −9 to day −5 and (bottom) triggering and organization of convection associated with the MJO‐like disturbances from day −4 to day 0.
Parameter sensitivities. Global distributions of vertically averaged ensemble spreads of 6 h (a–e) atmospheric temperature (unit: K) and (f–j) specific humidity (unit: g/kg), and the vertical variations of the globally averaged ensemble spreads ((g–o) for the temperature; (p–t) for the specific humidity). The ensemble spread of model state based on a perturbed parameter ensemble serves as a measure of the model's sensitivity to examined parameter. Each row represents the results for a single parameter.
CRTM‐simulated (17 September 0100 UTC) and (a) F16 SSMIS observed brightness temperatures (K) at SSMIS channel 9 (183 ± 7 GHz). (b) Uses cloud scattering properties with microphysics‐consistent spheres for all ice species (CRTM‐DS). (c, d) Results when spheres are substituted with sector snowflakes for all ice precipitation species (snow and graupel) and for only the snow species, respectively. (e, f) Use the same cloud scattering properties as Figure 3d (only the snow species using sector snowflake scattering properties) but with half of the snow and graupel water content, respectively.
Figure 1. Schematic illustration of (left) soil heat transport and (right) soil water transport. F is the soil heat flux, including heat conduction, convection of sensible heat with flowing liquid water, transfer of sensible heat and latent heat by diffusion of water vapor, these components are represented by subscript T, q(liq),SH, q(vap),SH and q(vap),LH, respectively. q is the water flux, including liquid water and vapor flux due to hydraulic potential (Ψ) gradient (which are represented by subscript Ψ,liq and Ψ,vap, respectively) and liquid water and vapor flux due to soil temperature (T) gradient (which are represented by subscript T,liq and T,vap, respectively). S is the sink term including transpiration. The red color in subscript means the impacts of heat transport on water flux, blue color in subscript means the impacts of water transport on heat flux.
Change in MSE (colors) and divergence of MSE flux (contours) owing to convective gustiness for JJA. Both h and ∇⋅(vh) are scaled by (1/Cp), where Cp is the specific heat of dry air at constant pressure, such that Δh is given in units of K and Δ(∇⋅(vh)) is given in units of K/d. All values are zonally averaged over 110–150E.
Present‐day annual precipitation amount median for CAM at (a, b) 110 km and (c, d) 28 km horizontal resolutions of (a, c) large‐scale and (b, d) convective contributions.
Cross‐lead error covariance matrix C(i, j) given by equation (3) for the real‐time CFSv2 forecasts of the Niño 3.4 index for monthly forecasts during the period 2011–2015. The axes give the lead time in units of days. Plot (a) shows the cross‐lead error covariance matrix for Niño 3.4 estimated directly from CFSv2 real‐time forecasts initialized at 0Z. Plot (b) shows the corresponding parametric model for cross‐lead error covariance matrix shown in Figure 4a. Similarly, plot (c) shows the cross‐lead error covariance matrix as in Figure 4a, but for real‐time forecasts initialized 4 times per day (0, 6, 12, 18 Z). Plot (d) shows the cross‐lead error covariance matrix for four initializations per day inferred from the parametric model fit at 1 day intervals.
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