Ben Heubl BenHeubl

## data.csv
Keys,DeviceName,Uniqueness,Uniqueness2,Benefits,Benefits2,DeviceDescription
246,3L Labs Footlogger,9,270,28,280,"The 3L Labs Footlogger is a wearable fitness tracking device that aims at spotting health problems early, as well as logging daily activity. Placed in the user's shoe, 8 sensors coupled to 1 accelerometer help identify and record exercise habits. The data is then disclosed to the user via text or smartphone app. This computing device's technology can be used for athletes training, regular everyday workouts and rehabilitation."
387,4D Force,5,150,13,130,"The 4D Force is a wearable technology that detects brain waves and converts them into electric signals. 4D Force developed a platform that can capture and compute high quality EEG/ EOG/EMG signals. With the device, users can control games by using the power of their thoughts. 4D Force can also be used for medical purposes as it has the ability to interpret electrical signals generated by the body, and create recommendations for changes in lifestyle.

## data.csv
Keys,DeviceName,Uniqueness,Uniqueness2,Benefits,Benefits2,DeviceDescription
246,3L Labs Footlogger,9,270,28,280,"The 3L Labs Footlogger is a wearable fitness tracking device that aims at spotting health problems early, as well as logging daily activity. Placed in the user's shoe, 8 sensors coupled to 1 accelerometer help identify and record exercise habits. The data is then disclosed to the user via text or smartphone app. This computing device's technology can be used for athletes training, regular everyday workouts and rehabilitation."
387,4D Force,5,150,13,130,"The 4D Force is a wearable technology that detects brain waves and converts them into electric signals. 4D Force developed a platform that can capture and compute high quality EEG/ EOG/EMG signals. With the device, users can control games by using the power of their thoughts. 4D Force can also be used for medical purposes as it has the ability to interpret electrical signals generated by the body, and create recommendations for changes in lifestyle.

## data3.csv

          
            innovationScore
            innovationScore2
            names

            
              4
              400
              Alcatel OneTouch Watch

            
              4
              400
              3L Labs Footlogger

            
              4
              400
              4iiii Viiiiva

            
              3.5
              350
              Air Scouter

            
              3.5
              350
              Amiigo Fitness Band

            
              4
              400
              Android Wear

            
              4.6
              460
              Athos

            
              3.5
              350
              AWatch Smartwatch

            
              3.5
              350
              BIA Sports Watch

## data.json
{
 "name": "Water",
 "children": [
  {
    "name": "US water footprint of national consumption", "size": 2842},
  {
    "name": "Germany water footprint of national consumption", "size": 1426},
  {
    "name": "Burundi water footprint of national consumption", "size": 719},
  {

## datafinal.csv

          
            Average Yearly Income
            Number of People Managed
            Average Satisfaction Rating (1to5)
            Percent of Stay Home Parents
            Personality Types
            types

            
              50153
              5.4
              2.67
              0.4
              1
              EXPLORERS

            
              32357
              1
              3
              1.2
              2
              EXPLORERS

            
              38338
              1.75
              3.16
              13.1
              3
              DIPLOMATS

            
              35660
              2.19
              3.25
              1.9
              4
              ANALYSTS

            
              40976
              2.82
              3.52
              13.1
              5
              DIPLOMATS

            
              51915
              2.8
              3.53
              4.2
              6
              ANALYSTS

            
              58990
              4.54
              3.62
              8.1
              7
              SENTINELS

            
              47850
              3.22
              3.64
              1.9
              8
              ANALYSTS

            
              77219
              5.49
              3.75
              3.1
              9
              SENTINELS

## aster_data.csv

          
            id
            order
            score
            weight
            color
            label

            
              FIS
              1.1
              59
              0.5
              #9E0041
              Guyana

            
              MAR
              1.3
              24
              0.5
              #C32F4B
              Mariculture

            
              NP
              3
              60
              1
              #F47245
              Natural Products

            
              CS
              4
              74
              1
              #FB9F59
              Carbon Storage

            
              CP
              5
              70
              1
              #FEC574
              Coastal Protection

            
              TR
              6
              42
              1
              #FAE38C
              Tourism &  Recreation

            
              LIV
              7.1
              77
              10
              #EAF195
              Livelihoods

            
              ECO
              7.3
              88
              0.5
              #C7E89E
              Economies

            
              ICO
              8.1
              60
              0.5
              #9CD6A4
              Iconic Species

## dataAT.csv

          
            year
            sales

            
              1989
              2.8

            
              1990
              1.5

            
              1991
              -8.7

            
              1992
              -4.2

            
              1993
              -4.1

            
              1994
              -2.6

            
              1995
              -2.5

            
              1996
              -3.4

            
              1997
              -0.4

## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body{
    width:1060px;
    margin:50px auto;
}
path {  stroke: #fff; }
path:hover {  opacity:0.9; }
rect:hover {  fill:blue; }

## datafinal4.csv
date,latitude,longitude,depth,mag,,magType,nst,gap,dmin,rms,net,id,updated,place,type
2015-04-28T16:39:39.080Z,-20.8661,-178.6384,579.35,6.1,,mww,,24,4.404,0.88,us,us200029x8,2015-04-29T01:56:22.000Z,"24km SSE of Ndoi Island, Fiji",earthquake
2015-04-26T07:09:10.010Z,27.782,85.9971,17.26,6.7,,mww,,19,4.894,0.99,us,us200029bt,2015-04-28T13:12:40.838Z,"19km SSE of Kodari, Nepal",earthquake
2015-04-25T06:45:21.380Z,28.1927,84.8645,14.62,6.6,,mb,,16,5.69,1.17,us,us2000292y,2015-04-29T02:17:05.757Z,"49km E of Lamjung, Nepal",earthquake
2015-04-25T06:11:26.270Z,28.1473,84.7079,15,7.8,,mww,,35,5.835,1.19,us,us20002926,2015-04-29T12:14:07.529Z,"34km ESE of Lamjung, Nepal",earthquake
2015-04-24T13:56:16.020Z,51.7685,-130.6951,10,6.2,,mww,,68,1.65,0.85,us,us200028ue,2015-04-25T15:27:59.537Z,"182km WSW of Bella Bella, Canada",earthquake
2015-04-24T03:36:43.230Z,-42.0632,172.9994,55,6.1,,mww,,22,0.308,0.97,us,us200028qc,2015-04-27T23:48:36.799Z,"68km NW of Kaikoura, New Zealand",earthquake
2015-04-22T22:57:15.970Z,-12.02

## data2.csv
date,place,deaths,mag,todrawcircles,mag3,explain,,
1902-04-19,Quezaltenango and San Marcos Guatemala,2000,7.5,3164.0625,35.15625,This quake also caused damage in Mexico at Tapachula Chiapas. It was felt as far away as Jalapa Veracruz and Mexico City. The duration in Mexico was estimated at one to one and a half minutes. [�307308A-51�],,
1902-12-16,Andijon (Andizhan) Uzbekistan (Turkestan Russia),4700,6.4,1677.7216,18.64135111,"Over 41000 buildings destroyed in the Andijon-Margilan area. A train was ""thrown from the tracks"" at Andijon station. A strong aftershock about 40 minutes later caused additional damage. [233�]",,
1903-04-28,Malazgirt Turkey (Ottoman Empire),3500,7,2401,26.67777778,About 12000 houses destroyed and 20000 animals killed in the Malazgirt-Patnos area. Slight damage as far away as Erzurum and Bitlis. A strong aftershock on August 6 caused additional casualties. [�21571�],,
1903-05-28,Gole Turkey (Ottoman Empire),1000,5.8,1131.6496,12.57388444,"Several villages destroyed. Death toll may be
	Keys,DeviceName,Uniqueness,Uniqueness2,Benefits,Benefits2,DeviceDescription
	246,3L Labs Footlogger,9,270,28,280,"The 3L Labs Footlogger is a wearable fitness tracking device that aims at spotting health problems early, as well as logging daily activity. Placed in the user's shoe, 8 sensors coupled to 1 accelerometer help identify and record exercise habits. The data is then disclosed to the user via text or smartphone app. This computing device's technology can be used for athletes training, regular everyday workouts and rehabilitation."
	387,4D Force,5,150,13,130,"The 4D Force is a wearable technology that detects brain waves and converts them into electric signals. 4D Force developed a platform that can capture and compute high quality EEG/ EOG/EMG signals. With the device, users can control games by using the power of their thoughts. 4D Force can also be used for medical purposes as it has the ability to interpret electrical signals generated by the body, and create recommendations for changes in lifestyle.
innovationScore	innovationScore2	names
4	400	Alcatel OneTouch Watch
4	400	3L Labs Footlogger
4	400	4iiii Viiiiva
3.5	350	Air Scouter
3.5	350	Amiigo Fitness Band
4	400	Android Wear
4.6	460	Athos
3.5	350	AWatch Smartwatch
3.5	350	BIA Sports Watch
	{
	"name": "Water",
	"children": [
	{
	"name": "US water footprint of national consumption", "size": 2842},
	{
	"name": "Germany water footprint of national consumption", "size": 1426},
	{
	"name": "Burundi water footprint of national consumption", "size": 719},
	{
Average Yearly Income	Number of People Managed	Average Satisfaction Rating (1to5)	Percent of Stay Home Parents	Personality Types	types
50153	5.4	2.67	0.4	1	EXPLORERS
32357	1	3	1.2	2	EXPLORERS
38338	1.75	3.16	13.1	3	DIPLOMATS
35660	2.19	3.25	1.9	4	ANALYSTS
40976	2.82	3.52	13.1	5	DIPLOMATS
51915	2.8	3.53	4.2	6	ANALYSTS
58990	4.54	3.62	8.1	7	SENTINELS
47850	3.22	3.64	1.9	8	ANALYSTS
77219	5.49	3.75	3.1	9	SENTINELS
id	order	score	weight	color	label
FIS	1.1	59	0.5	#9E0041	Guyana
MAR	1.3	24	0.5	#C32F4B	Mariculture
NP	3	60	1	#F47245	Natural Products
CS	4	74	1	#FB9F59	Carbon Storage
CP	5	70	1	#FEC574	Coastal Protection
TR	6	42	1	#FAE38C	Tourism & Recreation
LIV	7.1	77	10	#EAF195	Livelihoods
ECO	7.3	88	0.5	#C7E89E	Economies
ICO	8.1	60	0.5	#9CD6A4	Iconic Species
	year	sales
	1989	2.8
	1990	1.5
	1991	-8.7
	1992	-4.2
	1993	-4.1
	1994	-2.6
	1995	-2.5
	1996	-3.4
	1997	-0.4
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>
	body{
	width:1060px;
	margin:50px auto;
	}
	path { stroke: #fff; }
	path:hover { opacity:0.9; }
	rect:hover { fill:blue; }
	date,latitude,longitude,depth,mag,,magType,nst,gap,dmin,rms,net,id,updated,place,type
	2015-04-28T16:39:39.080Z,-20.8661,-178.6384,579.35,6.1,,mww,,24,4.404,0.88,us,us200029x8,2015-04-29T01:56:22.000Z,"24km SSE of Ndoi Island, Fiji",earthquake
	2015-04-26T07:09:10.010Z,27.782,85.9971,17.26,6.7,,mww,,19,4.894,0.99,us,us200029bt,2015-04-28T13:12:40.838Z,"19km SSE of Kodari, Nepal",earthquake
	2015-04-25T06:45:21.380Z,28.1927,84.8645,14.62,6.6,,mb,,16,5.69,1.17,us,us2000292y,2015-04-29T02:17:05.757Z,"49km E of Lamjung, Nepal",earthquake
	2015-04-25T06:11:26.270Z,28.1473,84.7079,15,7.8,,mww,,35,5.835,1.19,us,us20002926,2015-04-29T12:14:07.529Z,"34km ESE of Lamjung, Nepal",earthquake
	2015-04-24T13:56:16.020Z,51.7685,-130.6951,10,6.2,,mww,,68,1.65,0.85,us,us200028ue,2015-04-25T15:27:59.537Z,"182km WSW of Bella Bella, Canada",earthquake
	2015-04-24T03:36:43.230Z,-42.0632,172.9994,55,6.1,,mww,,22,0.308,0.97,us,us200028qc,2015-04-27T23:48:36.799Z,"68km NW of Kaikoura, New Zealand",earthquake
	2015-04-22T22:57:15.970Z,-12.02
	date,place,deaths,mag,todrawcircles,mag3,explain,,
	1902-04-19,Quezaltenango and San Marcos Guatemala,2000,7.5,3164.0625,35.15625,This quake also caused damage in Mexico at Tapachula Chiapas. It was felt as far away as Jalapa Veracruz and Mexico City. The duration in Mexico was estimated at one to one and a half minutes. [�307308A-51�],,
	1902-12-16,Andijon (Andizhan) Uzbekistan (Turkestan Russia),4700,6.4,1677.7216,18.64135111,"Over 41000 buildings destroyed in the Andijon-Margilan area. A train was ""thrown from the tracks"" at Andijon station. A strong aftershock about 40 minutes later caused additional damage. [233�]",,
	1903-04-28,Malazgirt Turkey (Ottoman Empire),3500,7,2401,26.67777778,About 12000 houses destroyed and 20000 animals killed in the Malazgirt-Patnos area. Slight damage as far away as Erzurum and Bitlis. A strong aftershock on August 6 caused additional casualties. [�21571�],,
	1903-05-28,Gole Turkey (Ottoman Empire),1000,5.8,1131.6496,12.57388444,"Several villages destroyed. Death toll may be