Plotly is a web-based visualization and data analysis platform. Sharing Plotly plots in your dataverse creates a rich experience in which each user can interact with the data in whatever way best fits their needs: from zooming in and examining individual data points, to changing colors and styles, to importing the raw data into their favorite software environment for further analysis.

1. Sharing data through plots
with Plotly
Alex Johnson
CTO, Plotly

2. A Plotly figure contains:
• the data, in as raw a form as possible
• a description of how to display that data
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figure into their favorite analysis language
The goal: Share data so it’s accessible to all
• Maintain the network of sources, acquisition/analysis/simulation
scripts, related information, and references
• Let each reader interact with the data in their own way
• The figure is the most compact and meaningful representation of
the data
• Keep the data with the figure

3. An interactive article
spin ‘‘flip’’ is accompanied by a nuclear spin
with mI 1. The change in Bnuc associated
s flip-flop transition is along the direction of exter-
, taking into account the positive nuclear g factors
nd As [2]. The cyclic repetition of the transition
to T can thereby lead to a finite time-averaged
ented along the external field.
value of detuning where the S-T anticrossing
denoted [see Fig. 1(b)], is a sensitive function
for jBtotj 80 mT, providing a straightforward
f measuring Bnuc within that range. To calibrate
surement, the dependence of on external field
de B0 is measured using the pulse sequence shown
(c): The 0; 2 S state is first prepared at point P. A
zed singlet in (1,1) is created by moving to point S
g S) via point P0. The system is held at point S for
S T2 then moved to point M and held there for
est part of the cycle. The sequence is then repeated.
S , rapid mixing of S and T states occurs.
he system is moved to point M, the (1,1) charge
l return to (0,2) only if the separated spins are in a
onfiguration. The probability of being in the sin-
e after time S thus appears as charge signal—the
ce between the (1,1) and (0,2) charge states—
by measuring the time-averaged QPC conduc-
S [see Fig. 1(a)]. Figure 1(c) shows gS as a function
nd VR with this pulse sequence applied. The field
nce of this signal is shown in Fig. 1(d), which plots
rated singlet state probability, PS, as a function of
and electron Zeeman energies would prevent flip-flop pro-
cesses [23].
We begin by examining the statistical polarization se-
quence shown in Fig. 2(a). We first measure PS as a
function of B0 and S, with S 100 ns, P 300 ns,
P0 100 ns, M 32 s. These data, shown in Fig. 3(a),
map out the S-T anticrossing in the limit of minimal
measurement induced polarization (polarization is negli-
gible for M > 30 s, as will be shown below). A steady-
state nuclear polarization at B0 24 mT results in a shift
in the position of , depending on the measurement time
M, which determines the cycle period [Fig. 3(b)]. As M
decreases, the position of moves to larger values of
detuning, indicating an increase in the average Bnuc. For
M > 30 s, the value of saturates to its unpolarized
value. Values for Bnuc M can be extracted from calibrat-
Adiabatic nuclear polarizationProbabilistic nuclear polarization
Rapid Adiabatic Passage
Slow Adiabatic Passage
Prepare Singlet
Rapid Adiabatic Passage, S-T+ evolution
PS
Prepare Singlet
t= S
t=0
Rapid Adiabatic Passage, Projection
a) b)
t= S
t=0
1-PS
Energy
(0,2)S
0
(0,2)S T0
T+
T-Energy
(0,2)S
0
(0,2)S T0
T+
T-
Energy
(0,2)S
0
(0,2)S T0
T+
T-
Energy
(0,2)S
0
(0,2)S T0
T+
T-
Energy
(0,2)S
0
(0,2)S T0
T+
T-
S
Energy
(0,2)S
0
(0,2)S T0
T+
T-
F
FIG. 2 (color online). (a) Probabilistic nuclear polarization
sequence. (b) Adiabatic nuclear polarization sequence (see text).
ability diagram (see text). The bright signal at point M
parallel to the 0 line is a result of Pauli-blocked
0; 2 charge transitions and reflects S-T mixing at
(d) PS S 200 ns as a function of S and B0. The
endent ‘‘spin funnel’’ corresponds to the S-T anti-
position, S .
067601-2
https://plot.ly/~alex/484

4. Keep the data with the figure

5. Keep the data with the figure

6. EXTRAS
Sources
Ice cream plants
Quandl:USDANASS/NASS_ICECREAMPRODUCTIONMEASUREDINPLANTS
Ice cream gallons
Quandl:USDANASS/NASS_ICECREAMPRODUCTIONMEASUREDINGALLONS
US population
Quandl:MWORTH/0_3
Extras
AJPM: Ice Cream Illusions - Wansink et al. 2006
IC_dynamics.py
Alex Johnson Dataverse: I scream, you scream!
Sourced by
JackParmer: Ice cream trend analysis
JackParmer: The golden age of ice cream
MattSundquist: Global ice cream demand
The data and analysis network

7. Interact with the data your way

8. Interact with the data your way

9. Interact with the data your way

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