邻居小女孩对马库斯的父亲口出恶言，马库斯决定给她一个教训，将她诱骗到一个个深坑附近推下。本片根据真人真事改编，从孩子的视角出发，以纪录式的影像风格和曲折复杂的故事，呈现了小镇社区的道德边界。

based on the story «The Well” by Abish Kekilbayev,It tells of the problems that matter to many people: rivalry, arrogance, love, friendship and treachery

《深井食堂》是一部情景喜剧，故事围绕一家 24 小时营业的餐厅展开。白天，这里供应西式简餐和各类饮料，其中以咖啡因含量比星巴克高出 100 倍的“疯狂咖啡”闻名；晚上则为加班人群提供宵夜。 几乎每天，餐厅里都会有人突然出现各种精神疾病症状。那些“不放弃治疗”的顾客，带来了一系列爆笑、荒诞又奇葩的故事。每集结尾，餐厅还会精心推出一道“惊”心配制的“深井名菜”，秉持药食同源的古法理论，仿佛要让没病的人进来后生出病，有病的人进来后换一种病。 餐厅里有自嗨狂老板顾德、控制欲过度的主厨毛学旺、患有强迫症的服务员江小白，以及有幻想症的收银员阿曼达（Amanda）。在夜黑风高或雾霾弥漫的夜晚，他们接待着一个个来自“深井”的夜不归人。这些带着都市病的客人，拖着“病躯”上演了一幕幕令人捧腹又荒诞的情节。他们不仅执着地尝试改变屏幕内顾客的状况，也在一定程度上影响着屏幕外的观众。

该剧讲述的是一家24小时营业的餐厅，白天供应西式简餐和各色饮料，以比星巴克高出100倍的咖啡因含量的Crazy Coffee闻名，晚上则是为了加班人群供应宵夜。几乎每一天这里都会有人突发各种精神疾病症状，“不弃疗”的他们带来一出又一出爆笑、 荒诞、奇葩的故事，每集结尾还会奉上一道“惊”心配制的“深井名菜”，以药食同源的古法理论让没病的进来有病的出去，有病的进来换一种病出去。作为情景喜剧，《深井食堂》讲述带着不同都市病的顾客闯入深井食堂后发生的鸡飞狗跳的趣事，荒诞中带着爆笑，爆笑中带着感动。自嗨狂老板顾德，过度控制欲的主厨毛学旺，强迫症服务员江小白，幻想症收银员Amanda。在夜黑风高或者雾霾深锁的晚上，接待服务着一个又一个来自深井的夜不归人，他们拖着自病之身给我们带来一出又一出爆笑、荒诞、奇葩的故事，每集结尾，他们会奉上一心配制的“深井名菜”，以期用药食同源的古法理论让没病的进来有病的出去，有病换一种病出去。他们执着的改变着屏幕内的深井客，也改变着屏幕外的深井客。

In the near future, a micro-worker performs tasks that are suggested to her. Between data center maintenance and data processing, she hesitantly searches for ways to feel emotions. Devoid of social links, this world is saturated with data.

未来世界，秘密系统“白金数据”得到开发，日本全国人民的DNA数据被存储其中，进而借此构建了一个检举率100%、冤案率0%的理想社会。可即便如此尖端的系统…

未来世界，秘密系统“白金数据”得到开发，日本全国人民的DNA数据被存储其中，进而借此构建了一个检举率100%、冤案率0%的理想社会。可即便如此尖端的系统，也被代号为“NF13”的连环杀人案所难住，凶手犯案累累，且至今下落不明，甚至连“白金数据”的开发者蓼科兄妹也惨遭杀害。此后不久，特殊解析研究所的天才科学家神乐龙平（二宫和也 饰）对蓼科早树（水原希子 饰）尸体上提取的嫌疑人基因进行解析，竟发现真凶的矛头指向自己。凭借同事白鸟理沙（杏 饰）的帮助，神乐连忙逃脱，而以刑警浅间玲司（丰川悦司 饰）为代表的搜查一课早已在这座城市内部下天罗地网。在此过程中，神乐自身的秘密逐渐付出水面，真相更是出乎所有人意料…… 　　本片根据著名推理小说作家东野圭吾的同名原作改编。

A witty, exhilarating and mind-expanding exploration of the word of our times - data - with mathematician Dr Hannah Fry. Following in the footsteps of BBC Four's previous gleefully nerdy, award-winning maths films The Joy of Stats, Tails you Win - The Science of Chance and The Joy of Logic, this new high-tech romp reveals exactly what data is and how it is captured, stored, shared and made sense of. Fry also tells the story of the engineers of the data age, people most of us have never heard of despite the fact they brought about a technological and philosophical revolution. 　　For Hannah Fry, the joy of data is all about spotting patterns. She's Lecturer in the Mathematics of Cities at UCL as well as being the presenter of the BBC series Trainspotting Live and City in the Sky, and she sees data as the essential bridge between two universes - the tangible, noisy, messy world that we see and experience, and the clean, ordered, elegant world of maths, where everything can be captured beautifully with equations. 　　Along the way the film reveals the connection between Scrabble scores and online movie streaming, explains why a herd of Wiltshire dairy cows are wearing pedometers, and uncovers the remarkable network map of Wikipedia. What's the mystery link between 'marmalade' and 'One Direction'? 　　The Joy of Data also hails the giant contribution of Claude Shannon, the American mathematician and electrical engineer who, in an attempt to solve the problem of noisy telephone lines, devised a way to digitise all information. It was Shannon, father of the 'bit', who singlehandedly launched the 'information age'. Meanwhile, the green lawns of Britain's National Physical Laboratory host a race between its young apprentices in order to demonstrate how and why data moves quickly and successfully around modern data networks. It's all thanks to the brilliant technique first invented there in the 1960s by Welshman Donald Davies - packet switching - without which there would be no internet as we know it. 　　But what of the future, big data and artificial intelligence? Should we be worried by the pace of change, and what our own data could and should be used for? Ultimately, Fry concludes, data has empowered all of us. We must have machines at our side if we're to find patterns in the modern-day data deluge. But, Fry believes, regardless of AI and machine learning, it will always take us to find the meaning in them.